CN110998681A

CN110998681A - Generation and monitoring of fall arrest device events

Info

Publication number: CN110998681A
Application number: CN201880051226.0A
Authority: CN
Inventors: 马修·J·布莱克福德; 佐海布·哈密德; 罗纳德·D·杰斯密
Original assignee: 3M Innovative Properties Co
Current assignee: 3M Innovative Properties Co
Priority date: 2017-08-10
Filing date: 2018-08-09
Publication date: 2020-04-10
Also published as: TW201909961A; JP2020529894A; US11541256B2; EP3665664A4; AU2018315744A1; EP3665664B1; WO2019030708A1; CA3071617A1; AU2018315744B2; EP3665664A1; BR112020002720A2; US20200206550A1

Abstract

A fall arrest device, comprising: a device housing; a shaft within the housing; a rotating body assembly rotatably connected to the shaft, the rotating body assembly comprising a drum and a disk having at least one region of ferromagnetic material; an extendable lifeline connected to the drum; a magnetic sensor stationary relative to the device housing and positioned adjacent to the disk; and a magnet comprising a hard magnetic material. The magnet is positioned stationary relative to the device housing and the magnetic sensor, wherein the magnetic sensor is configured to detect a change in a magnetic field generated by the magnet as the disk rotates about the axis, the change in the magnetic field being caused by the at least one region of ferromagnetic material being in close proximity to the magnet as the disk rotates.

Description

Generation and monitoring of fall arrest device events

Technical Field

The present disclosure relates to safety devices, and more particularly to fall protection systems and apparatus.

Background

Fall protection systems and devices are important safety devices for workers operating at potentially hazardous or even deadly heights. For example, to help ensure safety in the event of a fall, workers often wear safety harnesses that are connected to a support structure having fall arrest devices such as lanyards, energy absorbers, self-retracting lifeline(s) (SRL), descenders, and the like. Fall arrest devices such as SRLs typically include a lifeline wrapped around a biased drum rotatably connected to a housing. Movement of the lifeline causes the drum to rotate as the lifeline extends out of and retracts into the housing. Examples of self-retracting lifelines include ULTRA-LOK self-retracting lifelines, NANO-LOK self-retracting lifelines, and REBEL self-retracting lifelines manufactured by 3M fall protection services.

Disclosure of Invention

In general, this disclosure describes techniques for monitoring and predicting safety events for fall arrest devices, such as SRLs. Generally, a security event may refer to the activity of a user of a personal protection device (PPE), the condition of the PPE, and the like. For example, in the context of a fall arrest device, a safety event may be misuse of the fall arrest device, the user of the fall equipment experiencing a fall, or the fall arrest device failing.

According to aspects of the present disclosure, the SRL may be configured to incorporate one or more electronic sensors for capturing data indicative of the operation of the SRL, the location of the SRL, or environmental conditions surrounding the SRL. In some examples, the electronic sensors may be configured to measure a length, velocity, acceleration, force, or a variety of other characteristics associated with a lifeline of the SRL, a position of the SRL, and/or environmental factors associated with an environment in which the SRL is located, generally referred to herein as usage data or obtained sensor data. The SRL may be configured to transmit the usage data to a management system configured to execute an analysis engine that applies the usage data (or at least a subset of the usage data) to a security model to predict, in real-time or near real-time, a likelihood of a security event associated with the SRL occurring while a user (e.g., a worker) is wearing the SRL for an activity. In this manner, these techniques provide tools to accurately measure and/or monitor the operation of the SRL, determine a predicted result based on the operation, and generate alerts, models, or rule sets that can be used to warn, or even avoid, potential impending security events in real-time or pseudo-real-time.

In one example, a fall arrest device comprises a device housing; a shaft within the device housing; a rotating body assembly rotatably connected to the shaft, the rotating body assembly comprising a disk and a drum, the disk comprising at least one region of ferromagnetic material; an extendable lifeline connected to and coiled around the drum, the lifeline configured to connect the fall arrest device to a user or a support structure, wherein extension of the lifeline causes the reel and the drum to rotate about an axis; a magnetic sensor positioned stationary relative to the device housing, the magnetic sensor positioned adjacent to the disk; and a magnet comprising hard magnetic material, the magnet being positioned stationary with respect to the device housing and the magnetic sensor, wherein the magnetic sensor is configured to detect a change in a magnetic field generated by the magnet as the disk rotates about the axis, the change in the magnetic field being caused by bringing the at least one region of ferromagnetic material into close proximity to the magnet as the disk rotates.

In one example, a fall arrest device comprises a device housing; a shaft within the device housing; a rotating body assembly rotatably connected to the shaft, the rotating body assembly comprising a disk and a drum, the disk comprising at least one region of ferromagnetic material; an extendable lifeline connected to and coiled around the drum, the lifeline configured to connect the fall arrest device to a user or a support structure, wherein extension of the lifeline causes the reel and the drum to rotate about an axis; a first magnetic sensor positioned stationary relative to the device housing, the first magnetic sensor positioned adjacent to the disk; a first magnet comprising hard magnetic material, the first magnet positioned stationary with respect to the device housing and the first magnetic sensor, wherein the first magnetic sensor is configured to detect a change in a first magnetic field generated by the first magnet as the disk rotates about the axis, the change in the first magnetic field being caused by the at least one region of ferromagnetic material being in close proximity to the first magnet as the disk rotates; a second magnetic sensor positioned stationary relative to the device housing, the second magnetic sensor positioned adjacent to the disk; a second magnet comprising hard magnetic material, the second magnet positioned stationary with respect to the device housing and the second magnetic sensor, wherein the second magnetic sensor is configured to detect a change in a second magnetic field generated by the second magnet as the disk rotates about the axis, the change in the second magnetic field being caused by the at least one region of ferromagnetic material being in close proximity to the second magnet as the disk rotates. The first and second magnetic sensors are positioned about 90 ° out of phase in an orthogonal encoding configuration, the first and second magnetic sensors being configured to determine a direction of rotation of the disk based on the orthogonal encoding configuration.

In one example, a method for obtaining data from a fall arrest device. The method comprises rotating a disc of the fall arrest device, wherein the fall arrest device comprises a device housing; a shaft within the device housing; a rotator assembly rotatably connected to the shaft, the rotator assembly comprising a disk and a drum, the disk comprising at least one region of ferromagnetic material; an extendable lifeline connected to the drum and coiled thereon, the lifeline configured to connect the fall arrest device to a user or a support structure, wherein the extension of the lifeline causes the disk and the drum to rotate about the shaft; a magnetic sensor positioned stationary relative to the device housing, the magnetic sensor positioned adjacent to the disk; and a magnet comprising a hard magnetic material, the magnet positioned stationary with respect to the device housing and the magnetic sensor, wherein the magnetism generates a magnetic field, and the processing circuitry is connected to the magnetic sensor; measuring, with the processing circuitry, an interruption of the magnetic field generated by the magnet using the magnetic sensor, wherein the interruption of the magnetic field is generated by rotating the disk such that at least one region of ferromagnetic material is in close proximity to the magnet or the magnetic sensor, thereby causing the magnetic sensor to measure a change in the magnetic field. The method further includes analyzing, with the processing circuit, the interruption of the measured magnetic field to determine at least one of a rotational angle of the disk, a number of rotations of the disk, a rotational speed of the disk, or a rotational acceleration of the disk.

The details of one or more examples of the disclosure are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the disclosure will be apparent from the description and drawings, and from the claims.

Drawings

Fig. 1 is a block diagram illustrating an example system in which a Personal Protective Equipment (PPE) with embedded sensors and communication capabilities is used within multiple work environments and managed by a PPE management system in accordance with various techniques of the present disclosure.

FIG. 2 is a block diagram illustrating an operational perspective view of the PPE management system shown in FIG. 1.

Fig. 3 is a block diagram illustrating one example of a self-retracting lifeline (SRL) according to aspects of the present disclosure.

Fig. 4 is a schematic diagram illustrating internal components of an example SRL.

Fig. 5A is a schematic diagram illustrating example magnetic field lines produced by an example magnet used in the SRL of fig. 4.

Fig. 5B is a schematic diagram illustrating example magnetic field lines produced by the example magnet of the SRL of fig. 4 when regions of ferromagnetic material are brought into close proximity.

Fig. 6-12 are schematic diagrams of example arrangements of disks, magnetic sensors, and magnets that may be incorporated in the SRL of fig. 4.

Fig. 13 is a diagram illustrating an example model applied by the personal protective equipment management system or other device herein to measure rope speed, acceleration, and rope length with respect to worker activity, wherein the model is arranged to define safe areas and to predict areas of unsafe behavior for safety events, in accordance with aspects of the present disclosure.

Fig. 14A and 14B are graphs illustrating example usage data from a worker determined by a personal protective equipment management system to represent low risk behavior and high risk behavior that triggers an alert or other response in accordance with aspects of the present disclosure.

Fig. 15 is a flow diagram illustrating an exemplary process for predicting the likelihood of a security event in accordance with aspects of the present disclosure.

Detailed Description

In accordance with aspects of the present disclosure, the SRL may be configured to incorporate one or more electronic sensors for capturing data indicative of operational, positional, or environmental conditions surrounding the SRL. Such data may be generally referred to herein as usage data, or alternatively, sensor data. The usage data may take the form of a stream of samples over a period of time. In some cases, the electronic sensor may be configured to measure a length, velocity, acceleration, force, or a variety of other characteristics associated with a lifeline of the SRL, position information indicative of a position of the SRL, and/or environmental factors associated with an environment in which the SRL is located. Further, as described herein, the SRL may be configured to include one or more electronic components such as a speaker, a vibration device, an LED, a buzzer, or other devices for outputting a communication to the individual workers, such as a warning, voice message, sound, indicator light, or the like.

In accordance with aspects of the present disclosure, the SRL may be configured to transmit the obtained usage data to a personal protective equipment management system (ppmms), which may be a cloud-based system having an analysis engine configured to process incoming usage data streams from SRLs or other personal protective equipment deployed and used by a population of workers at different work environments. The analytics engine of the ppmms may apply one or more models to the incoming flow of usage data (or at least a subset of the usage data) to monitor and predict the likelihood of a safety event occurring for a worker associated with any individual SRL. For example, the analysis engine may compare the measured parameters (e.g., measured by the electronic sensors) to known models that characterize the activities of the users of the SRL, such as activities that represent safe, unsafe, or activities of interest (which may typically occur before unsafe activities), in order to determine the probability of an event occurring.

The analysis engine may then generate an output in response to predicting the likelihood of the occurrence of the security event. For example, the analysis engine may generate an output indicative of a security event that may occur based on data collected by a user of the SRL. The output may be used to alert a user of the SRL that a security event is likely to occur, allowing the user to change or adjust his or her behavior. In other examples, a processor within a circuit embedded within the SRL or an intermediate data hub that is more local to the worker may be programmed via the ppmms or other mechanism to apply a model or set of rules determined by the ppmms to locally generate and output alerts or other precautions designed to avoid or mitigate the predicted security event. In this manner, the techniques provide tools to accurately measure and/or monitor the operation of the SRL and determine a predicted outcome based on the operation.

Fig. 1 is a block diagram illustrating an example computing system 2 that includes a personal protective equipment management system (ppmms) 6 for managing personal protective equipment. As described herein, the ppmms allow authorized users to perform preventative occupational health and safety measures, and manage the inspection and maintenance of safety equipment. By interacting with the PPEMS6, a safety professional may, for example, manage regional inspections, worker health, and safety compliance training.

Generally, the PPEMS6 provides data collection, monitoring, activity logging, reporting, predictive analysis, and alert generation. For example, the ppmms 6 includes a base analysis and security event prediction engine and an alert system according to various examples described herein. As described further below, the ppmms 6 provides a full suite of personal security guard device management tools, and implements the various techniques of this disclosure. That is, the PPEMS6 provides an integrated end-to-end system for managing personal protective equipment, such as safety equipment, used by workers 10 within one or more physical environments 8, which may be a construction site, a mining or manufacturing site, or any physical environment. The techniques of this disclosure may be implemented within various portions of computing environment 2.

As shown in the example of fig. 1, the system 2 represents a computing environment in which computing devices within a plurality of

physical environments

8A,8B (collectively referred to as environments 8) are in electronic communication with a ppmms 6 via one or more computer networks 4. Each of the physical environments 8 represents a physical environment, such as a work environment, in which one or more individuals, such as workers 10, utilize personal protective equipment while engaged in tasks or activities within the respective environment.

In this example, physical environment 8A is shown generally with workers, while environment 8B is shown in expanded form to provide a more detailed example. In the example of FIG. 1, a plurality of workers 10A-10N are shown utilizing respective fall arrest devices, which in this example are shown as Self Retracting Lifelines (SRLs) 11A-11N attached to a safety support structure 12.

As further described herein, each of the SRLs 11 includes embedded sensors or monitoring devices and processing electronics configured to capture data in real-time as a user (e.g., a worker) engages in an activity while wearing the fall arrest device. For example, as described in more detail with respect to the example shown in fig. 4, the SRL may include various electronic sensors, such as one or more of a magnetic sensor, an extension sensor, a tension sensor, an accelerometer, a position sensor, an altimeter, one or more environmental sensors, and/or other sensors for measuring the operation of the SRL 11. Further, each of the SRLs 11 may include one or more output devices for outputting data indicative of the operation of the SRL11 and/or generating and outputting communications to the respective worker 10. For example, SRL11 may include one or more devices used to generate audible feedback (e.g., one or more speakers), visual feedback (e.g., one or more displays, Light Emitting Diodes (LEDs), etc.), or tactile feedback (e.g., devices that vibrate or provide other tactile feedback).

Generally, each of the environments 8 includes a computing facility (e.g., a local area network) through which the SRL11 can communicate with the ppmms 6. For example, the physical environment 8 may be configured with wireless technologies, such as 802.11 wireless networks, 802.15ZigBee networks, and the like. In the example of fig. 1, the environment 8B includes a local network 7, the local network 7 providing a packet-based transport medium for communicating with the ppmms 6 via the network 4. Further, the physical environment 8B includes a plurality of

wireless access points

19A, 19B, which plurality of

wireless access points

19A, 19B may be geographically distributed throughout the environment to provide support for wireless communications throughout the operating environment 8B.

Each of the SRLs 11 is configured to communicate data such as sensed actions, events, and conditions via wireless communications, such as via an 802.11WiFi protocol, a bluetooth protocol, and so forth. The SRL11 may communicate directly with one of the

wireless access points

19A or 19B, for example. As another example, each worker 10 may be equipped with a respective one of the wearable communication hubs 14A-14N, which wearable communication hubs 14A-14N enable and facilitate communications between the SRL11 and the ppmms 6. For example, the SRL11 and other PPEs for the respective workers 10 may communicate with the respective communication hubs 14 via bluetooth or other short range protocols, and the communication hubs 14 may communicate with the PPEM 6 via wireless communications handled through the

wireless access points

19A or 19B. Although shown as a wearable device, the hub 14 may be implemented as a standalone device deployed within the physical environment 8B.

Generally, each of the hubs 14 serves as a wireless device for the SRL11 that relays communications with the SRL11, and may be able to buffer usage data in the event that the ppmms 6 lose communications. Further, each of the hubs 14 may be programmed via the ppmms 6 such that local alert rules may be installed and executed without requiring connection to the cloud network 4. Thus, each of the hubs 14 provides a relay for usage data flows from the SRL11 and/or other PPEs within the respective environment, and provides a local computing environment for localized alerts based on event flows in the event of loss of communication with the PPEMS 6.

As shown in the example of FIG. 1, an environment such as environment 8B may also include one or more wireless-enabled beacons 17A-17C that provide accurate location information within operating environment 8B. For example, the beacons 17A-17C may be GPS-enabled such that a controller within a respective beacon may be able to accurately determine the location of the respective beacon. Based on wireless communication with one or more of the beacons 17, a given SRL11 or communication hub 14 worn by the worker 10 is configured to determine the location of the worker within the work environment 8B. In this manner, event data reported to the PPEMS6 may be tagged with location information to aid in analysis, reporting, and resolution performed by the PPEMS.

Further, an environment such as environment 8B may also include one or more wireless-enabled sensing stations, such as

sensing stations

21A and 21B. Each sensing station 21 includes one or more sensors and a controller configured to output data indicative of the sensed environmental conditions. Further, the sensing stations 21 may be located within respective geographic regions of the environment 8B or otherwise interact with the beacons 17 to determine respective locations and include such location information in reporting the environmental data to the ppmms 6. Accordingly, the ppmms 6 may be configured to correlate sensed environmental conditions with a particular area, and thus may utilize captured environmental data in processing event data received from the SRL 11. For example, the ppmms 6 may utilize the environmental data to help generate alerts or other instructions for the SRL11 and for performing predictive analysis, such as determining any correlation between certain environmental conditions (e.g., heat, humidity, visibility) and abnormal worker behavior or increased safety events. As such, the ppmms 6 may utilize current environmental conditions to help predict and avoid impending security events. Exemplary environmental conditions that may be sensed by the sensing device 21 include, but are not limited to: temperature, humidity, presence of gas, pressure, visibility, wind, etc.

In an exemplary implementation, an environment such as environment 8B may also include one or more security stations 15, with one or more security stations 15 being distributed throughout the environment to provide viewing stations for accessing the ppmms 6. The security station 15 may allow one of the workers 10 to check the SRL11 and/or other security devices, verify that the security device is appropriate for the environment 8, and/or exchange a particular one of the data. For example, the security station 15 may transmit alert rules, software updates, or firmware updates to the SRL11 or other device. The security station 15 may also receive data cached on the SRL11, hub 14, and/or other PPEs. That is, while SRL11 (and/or data hub 14) may generally transmit usage data from sensors of SRL11 to network 4, in some instances SRL11 (and/or data hub 14) may not have connectivity to network 4. In such instances, SRL11 (and/or data hub 14) may store usage data locally and transmit the usage data to security station 15 upon proximity to security station 15. The security station 15 may then upload data from the SRL11 and connect to the network 4.

Further, each of the environments 8 includes computing facilities that provide an operating environment for the end-user computing devices 16 for interacting with the ppmms 6 via the network 4. For example, each of the environments 8 typically includes one or more security administrators responsible for overseeing security compliance within the environments. Generally, each user 20 interacts with the computing device 16 to access the ppmms 6. Similarly, a remote user 24 may use the computing device 18 to interact with the PPEMS via the network 4. For purposes of example, the end-user computing device 16 may be a laptop computer, a desktop computer, a mobile device such as a tablet computer or so-called smart phone, and so forth.

Users

20, 24 interact with the ppmms 6 to control and actively manage many aspects of the security devices used by workers 10, such as accessing and viewing usage records, analysis, and reports. For example, the

users

20, 24 may view usage information collected and stored by the PPEMS6, where the usage information may include: data specifying a start time and an end time within a certain duration (e.g., a day, a week, etc.), data collected during a particular event (such as a detected fall), sensed data collected from a user, environmental data, and so forth. Further, the

users

20, 24 may interact with the PPEMS6 to perform asset tracking and schedule maintenance events for individual pieces of security equipment (e.g., SRL 11) to ensure compliance with any procedures or regulations. The PPEMS6 may allow the

users

20, 24 to create and complete digital checklists with respect to maintenance procedures and synchronize any results of these procedures from the computing devices 16, 18 to the PPEMS 6.

Furthermore, as described herein, the ppmms 6 integrate an event processing platform configured to process thousands or even millions of concurrent event streams from digitally enabled PPEs such as SRL 11. The underlying analysis engine of the ppmms 6 applies historical data and models to the inbound streams to compute assertions, such as abnormal or predicted security event occurrences identified based on the condition or behavioral patterns of the workers 11. Additionally, the PPEMS6 provides real-time alerts and reports to notify the worker 10 and/or the

users

20, 24 of any predicted events, anomalies, trends, and so forth.

The analysis engine of the ppmms 6 may, in some examples, apply analysis to identify relationships or correlations between sensed operational data, environmental conditions, geographic areas, and other factors, and to analyze the impact on security events. The ppmms 6 may determine, based on data obtained throughout the worker population 10, which particular activities that are likely to be within a certain geographic area result in or predict the occurrence of an abnormally high safety event.

In this manner, the PPEMS6 tightly integrates a comprehensive tool for managing personal protective equipment through an underlying analysis engine and communication system to provide data acquisition, monitoring, activity logging, reporting, behavioral analysis, and alert generation. In addition, the PPEMS6 provides a communication system between the various elements of the system 2 that is operated and utilized by these elements. The

user

20, 24 may access the PPEMS to view the results of any analysis performed by the PPEMS6 on the data obtained from the worker 10. In some examples, the ppmms 6 may present a web-based interface via a web server (e.g., an HTTP server), or may deploy client applications for devices of the computing devices 16, 18 used by the users 20, 24 (such as desktop computers, laptop computers, mobile devices such as smartphones and tablets, and so forth).

In some examples, the POEMS 6 may provide a database query engine for querying the POEMS 6 directly to view the acquired security information, compliance information, and any results of the analysis engine, e.g., via a dashboard, warning notifications, reports, etc. That is, the users 24, 26 or software executing on the computing devices 16, 18 may submit queries to the PPEMS6 and receive data corresponding to these queries for presentation in the form of one or more reports or dashboards. Such dashboards may provide various insights about the system 2, such as baseline ("normal") operation throughout a population of workers, identification of any abnormal worker engaged in abnormal activities that may expose the worker to risk, identification of any geographic region within the environment 2 for which a significant abnormal (e.g., high) safety event has occurred or is predicted to occur, identification of any of the environments 2 that exhibit abnormal occurrence of safety events relative to other environments, and so forth.

As discussed further below, the ppmms 6 may simplify the workflow of individuals responsible for monitoring and ensure the safety compliance of an entity or environment to allow an organization to take preventative or corrective action for certain areas within the environment 8, specific SRLs 11 or individual workers 10, define and may further allow the entity to implement a workflow program that is data driven by the underlying analysis engine.

As one example, the underlying analysis engine of the ppmms 6 may be configured to compute and present customer-defined metrics for a population of workers within a given environment 8 or across multiple environments for an entire organization. For example, the ppmms 6 may be configured to acquire data and provide aggregate performance metrics and predictive behavioral analysis throughout a population of workers (e.g., in the workers 10 of either or both of the

environments

8A, 8B). Also, the

users

20, 24 may set benchmarks for any security incidents to occur, and the ppmms 6 may track actual performance metrics relative to benchmarks for individual or defined groups of workers.

As another example, if certain combinations of conditions exist, the ppmms 6 may further trigger an alert, for example, to expedite inspection or repair of one of the safety devices, such as the SRL 11. In this manner, the ppmms 6 may identify individual SRLs 11 or workers 10 for which the metrics do not meet the benchmark, and prompt the user to intervene and/or execute procedures to improve the metrics relative to the benchmark, thereby ensuring compliance and proactively managing the safety of the workers 10.

Fig. 2 is a block diagram providing a perspective view of the operation of the ppmms 6 when hosted as a cloud-based platform capable of supporting a variety of different work environments 8 with an entire population of workers 10 having various communication-enabled personal protective equipment (PPEs 62), such as Safety Release Lines (SRLs) 11A-11N or other safety equipment. In the example of fig. 2, the components of the ppmms 6 are arranged in accordance with a plurality of logical layers implementing the techniques of the present disclosure. Each layer may be implemented by one or more modules comprising hardware, software, or a combination of hardware and software.

In fig. 2, PPEs 62, such as SRLs 11 and/or other equipment, operate as clients 63, either directly or through hub 14 and computing device 60, with clients 63 communicating with the ppmms 6 via interface layer 64. Computing device 60 typically executes client software applications, such as desktop applications, mobile applications, and web applications. Computing device 60 may represent either of computing devices 16, 18 of fig. 1. Examples of computing device 60 may include, but are not limited to, portable or mobile computing devices (e.g., smartphones, wearable computing devices, tablets), laptop computers, desktop computers, smart television platforms, and servers, to name a few.

As additionally described in this disclosure, the PPE 62 communicates with the ppmms 6 (either directly or via the hub 14) to provide data streams obtained from embedded sensors and other monitoring circuitry, and to receive alerts, configuration, and other communications from the ppmms 6. Client applications executing on the computing device 60 may communicate with the PPEMS6 to send and receive information retrieved, stored, generated, and/or otherwise processed by the service 68. For example, the client application may request and edit security event information that includes analysis data stored at the PPEMS6 and/or managed by the PPEMS 6. In some examples, the client application may request and display total security event information that summarizes or otherwise aggregates multiple individual instances of the security event and corresponding data obtained from the PPEs 62 and/or generated by the ppmms 6. The client application may interact with the PPEMS6 to query for analytical information about past and predicted security events, trends in the behavior of the worker 10, to name a few. In some examples, the client application may output display information received from the ppmms 6 to visualize such information to a user of the client 63. As further illustrated and described below, the ppmms 6 may provide information to a client application that outputs the information for display in a user interface.

Client applications executing on computing device 60 may be implemented for different platforms but include similar or identical functionality. For example, the client application may be a desktop application such as Microsoft Windows, Apple OS x, or Linux, compiled to run on a desktop operating system, to name a few. As another example, the client application may be a mobile application compiled to run on a mobile operating system, such as Google Android, Apple iOS, Microsoft Windows mobile, or BlackBerry OS, to name a few. As another example, the client application may be a web application, such as a web browser that displays a web page received from the ppmms 6. In the example of a web application, the PPEMS6 may receive a request from the web application (e.g., a web browser), process the request, and send one or more responses back to the web application. In this manner, the collection of web pages, the web application of client-side processing, and the server-side processing performed by the PPEMS6 collectively provide functionality to perform the techniques of this disclosure. In this manner, client applications use the various services of the PPEMS6 in accordance with the techniques of this disclosure, and these applications may operate within a variety of different computing environments (e.g., the PPE's embedded circuits or processors, a desktop operating system, a mobile operating system, or a web browser, to name a few).

As shown in fig. 2, the ppmms 6 includes an interface layer 64, the interface layer 64 representing an Application Programming Interface (API) or a set of protocol interfaces presented and supported by the ppmms 6. The interface layer 64 initially receives messages from any of the clients 63 for further processing at the ppmms 6. Thus, the interface layer 64 may provide one or more interfaces available to client applications executing on the client 63. In some examples, the interface may be an Application Programming Interface (API) accessed over a network. The interface layer 64 may be implemented with one or more web servers. One or more web servers may receive incoming requests, process and forward information from the requests to the service 68, and provide one or more responses to the client application that originally sent the request based on the information received from the service 68. In some examples, one or more web servers implementing interface layer 64 may include a runtime environment to deploy program logic that provides one or more interfaces. As described further below, each service may provide a set of one or more interfaces that are accessible via the interface layer 64.

In some examples, the interface layer 64 may provide a representational state transfer (RESTful) interface that interacts with services and manipulates resources of the ppmms 6 using HTTP methods. In such examples, the service 68 may generate a JavaScript object notification (JSON) message that the interface layer 64 sends back to the client application that submitted the initial request. In some examples, the interface layer 64 provides a web service that uses Simple Object Access Protocol (SOAP) to process requests from client applications. In other examples, the interface layer 64 may use Remote Procedure Calls (RPCs) to process requests from the client 63. Upon receiving a request from a client application to use one or more services 68, the interface layer 64 sends information to the application layer 66 that includes the services 68.

As shown in fig. 2, the ppmms 6 also includes an application layer 66 that represents a collection of services for implementing most of the basic operations of the ppmms 6. The application layer 66 receives information included in requests received from client applications and further processes the information in accordance with one or more of the services 68 invoked by the requests. The application layer 66 may be implemented as one or more discrete software services executing on one or more application servers (e.g., physical or virtual machines). That is, the application server provides a runtime environment for executing the service 68. In some examples, the functionality of the functional interface layer 64 and the application layer 66 as described above may be implemented at the same server.

The application layer 66 may include one or more independent software services 68, such as processes that communicate via a logical service bus 70 as one example. Service bus 70 generally represents a logical interconnection or set of interfaces that allow different services to send messages to other services, such as through a publish/subscribe communications model. For example, each of the services 68 may subscribe to a particular type of message based on criteria for the respective service. When a service publishes a particular type of message on the service bus 70, other services subscribing to that type of message will receive the message. In this manner, each of the services 68 may communicate information with each other. As another example, the service 68 may communicate in a point-to-point manner using sockets or other communication mechanisms. In other examples, a pipeline system architecture may be used to enforce workflow and logic processing of data or messages as they are processed by software system services. Before describing the functionality of each of the services 68, the layers are briefly described herein.

The data layer 72 of the PPEMS6 represents a data repository that provides persistence for information in the PPEMS6 using one or more data repositories 74. A data repository may generally be any data structure or software that stores and/or manages data. Examples of data repositories include, but are not limited to, relational databases, multidimensional databases, maps, and hash tables, to name a few. The data layer 72 may be implemented using relational database management system (RDBMS) software to manage information in the data repository 74. The RDBMS software may manage one or more data repositories 74 that are accessible using Structured Query Language (SQL). The information in one or more databases may be stored, retrieved and modified using RDBMS software. In some examples, the data layer 72 may be implemented using an object database management system (ODBMS), an online analytical processing (OLAP) database, or other suitable data management system.

As shown in FIG. 2, each of the services 68A-68I ("services 68") is implemented in a modular fashion within the PPEMS 6. Although shown as separate modules for each service, in some examples, the functionality of two or more services may be combined into a single module or component. Each of the services 68 may be implemented in software, hardware, or a combination of hardware and software. Further, the services 68 may be implemented as separate devices, separate virtual machines or containers, processes, threads, or software instructions typically for execution on one or more physical processors.

In some examples, one or more of the services 68 may each provide one or more interfaces exposed through the interface layer 64. Accordingly, client applications of computing device 60 may invoke one or more interfaces of one or more of services 68 to perform the techniques of this disclosure.

In accordance with the techniques of this disclosure, the service 68 may include an event processing platform including an event endpoint front end 68A, an event selector 68B, an event handler 68C, and a High Priority (HP) event handler 68D. Event endpoint front end 68A operates as a front end interface for communications received and sent to PPEs 62 and hub 14. In other words, the event endpoint front end 68A operates as a front line interface to safety equipment deployed within the environment 8 and used by the worker 10. In some examples, event endpoint front end 68A may be implemented as a plurality of tasks or jobs generated to receive respective inbound communications of event stream 69 from PPEs 62, PPEs 62 carrying data sensed and captured by security devices. For example, when receiving the event stream 69, the event endpoint front end 68A may derive the task of quickly enqueuing inbound communications (referred to as an event) and closing the communication session, thereby providing high speed processing and scalability. For example, each incoming communication may carry recently captured data representing sensed conditions, motion, temperature, motion, or other data (commonly referred to as events). The communications exchanged between the event endpoint front end 68A and the PPE may be real-time or pseudo-real-time, depending on communication delay and continuity.

Event selector 68B operates on event streams 69 received from PPEs 62 and/or hub 14 via front end 68A and determines a priority associated with an incoming event based on a rule or classification. Based on the priority, the event selector 68B enqueues the events for subsequent processing by the event handler 68C or a High Priority (HP) event handler 68D. Additional computing resources and objects may be dedicated to the HP event handler 68D in order to ensure response to critical events, such as improper use of PPE, use of filters and/or respirators that are inappropriate based on geographic location and conditions, failure to properly secure the SRL11, and so forth. In response to processing a high priority event, HP event handler 68D may immediately invoke notification service 68E to generate an alert, instruction, warning, or other similar message for output to SRL11, hub 14, and/or

remote users

20, 24. Events not classified as high priority are consumed and processed by event handler 68C.

Generally speaking, the event handler 68C or the High Priority (HP) event handler 68D operates on incoming event streams to update event data 74A within the data repository 74. In general, event data 74A may include all or a subset of the usage data obtained from PPE 62. For example, in some instances, event data 74A may comprise the entire stream of data samples obtained from electronic sensors of PPE 62. In other cases, event data 74A may include a subset of such data that is associated with a particular time period or activity of PPE 62, for example. Event handlers 68C, 68D may create, read, update, and delete event information stored in event data 74A. Event information may be stored in a corresponding database record as a structure including name/value pairs of the information, such as a data table specified in a row/column format. For example, the name (e.g., column) may be "worker ID" and the value may be an employee identification number. The event record may include information such as, but not limited to: worker identification, PPE identification, acquisition timestamp, and data indicative of one or more sensed parameters.

Further, the event selector 68B directs the incoming event stream to a flow analysis service 68F, which flow analysis service 68F represents an example of an analysis engine configured to perform deep processing on the incoming event stream to perform real-time analysis. The flow analysis service 68F may, for example, be configured to process and compare multiple flows of event data 74A with historical data and models 74A in real-time as the event data 74A is received. In this manner, the flow analysis service 68F may be configured to detect anomalies, transform incoming event data values, and trigger alerts when safety issues are detected based on conditions or worker behavior. Historical data and model 74B may include, for example, specified security rules, business rules, and the like. In this manner, the historical data and model 74B may characterize the activities of the users of the SRL11, such as adherence to security rules, business rules, and so forth. Further, the flow analysis service 68F may generate output for communication with the PPPE 62 through the notification service 68E or the computing device 60 through the record management and reporting service 68G.

In this manner, analysis service 68F processes inbound event streams, possibly hundreds or thousands of event streams, from enabled security PPEs 62 utilized by workers 10 within environment 8 to apply historical data and models 74B to compute a predicted occurrence of an assertion, such as an identified anomaly or an impending security event, based on the condition or behavioral pattern of the worker. Analysis service 68F may issue the assertion to notification service 68E and/or record management over service bus 70 for output to any of clients 63.

In this manner, the analytics service 68F may be configured as an active security management system that predicts impending security issues and provides real-time alerts and reports. Further, the analytics service 68F may be a decision support system that provides techniques for processing inbound streams of event data to generate assertions in the form of statistics, conclusions, and/or suggestions on an aggregated or personalized human and/or PPE basis for enterprises, security officers, and other remote users. For example, the analytics service 68F may apply the historical data and models 74B to determine the likelihood that a safety event is imminent for a particular worker based on detected behavior or activity patterns, environmental conditions, and geographic location. In some examples, analysis service 68F may determine whether the worker is currently injured, for example, due to fatigue, illness, or alcohol/drug use, and may require intervention to prevent a safety event. As another example, the analytics service 68F may provide comparative ratings of worker or safety device types in a particular environment 8.

Accordingly, the analytics service 68F may maintain or otherwise use one or more models that provide risk metrics to predict security events. The analysis service 68F may also generate order sets, recommendations, and quality measures. In some examples, the analytics service 68F may generate a user interface based on the processing information stored by the ppmms 6 to provide actionable information to any of the clients 63. For example, the analytics service 68F may generate dashboards, warning notifications, reports, and the like for output at any of the clients 63. Such information may provide various insights about: baseline ("normal") operation throughout a population of workers, identification of any abnormal worker that may expose the worker to abnormal activity at risk, identification of any geographic region within an environment for which significant abnormal (e.g., high) safety events have occurred or are predicted to occur, identification of any of the environments exhibiting abnormal occurrence of safety events relative to other environments, and so forth.

While other techniques may be used, in one example implementation, the analytics service 68F utilizes machine learning in operating on the security event stream in order to perform real-time analytics. That is, the analytics service 68F includes executable code generated by applying machine learning to training event stream data and known security events to detect patterns. The executable code may take the form of software instructions or a set of rules and is generally referred to as a model, which may then be applied to the event stream 69 for detecting similar patterns and predicting impending events.

In some examples, analysis service 68F may generate individual models for particular workers, particular worker groups, particular environments, or combinations thereof. Analysis service 68F may update the model based on the usage data received from PPE 62. For example, analysis service 68F may update a model for a particular worker, a particular group of workers, a particular environment, or a combination thereof based on data received from PPE 62.

Alternatively or in addition, the analytics service 68F may communicate all or part of the generated code and/or machine learning model to the hub 14 (or PPE 62) for execution thereon to provide local alerts to the PPEs in near real-time. Example machine learning techniques that may be used to generate model 74B may include various learning approaches such as supervised learning, unsupervised learning, and semi-supervised learning. Exemplary types of algorithms include bayesian algorithms, clustering algorithms, decision tree algorithms, regularization algorithms, regression algorithms, instance based algorithms, artificial neural network algorithms, deep learning algorithms, dimension reduction algorithms, and the like. Various examples of specific algorithms include bayesian linear regression, boosted decision tree regression and neural network regression, back propagation neural networks, Apriori algorithms, K-means clustering, K-nearest neighbor (kNN), Learning Vector Quantization (LVQ), self-organised maps (SOM), Local Weighted Learning (LWL), ridge regression, Least Absolute Shrinkage and Selection Operators (LASSO), elastic networks and Least Angle Regression (LARS), Principal Component Analysis (PCA) and Principal Component Regression (PCR).

The record management and reporting service 68G processes and responds to messages and queries received from the computing device 60 via the interface layer 64. For example, the record management and reporting service 68G may receive requests from client computing devices for event data related to individual workers, groups or sample sets of workers, geographic areas of the environment 8 or the entire environment 8, individuals or groups/types of PPEs 62. In response, the record management and reporting service 68G accesses the event information based on the request. In retrieving event data, the record management and reporting service 68G constructs an output response to the client application that initially requested the information. In some examples, the data may be included in a document, such as an HTML document, or the data may be encoded in JSON format or rendered by a dashboard application executing on the requesting client computing device. For example, as further described in this disclosure, an exemplary user interface including event information is depicted in the figures.

As a further example, the record management and reporting service 68G may receive a request to look up, analyze, and correlate PPE event information. For example, the record management and reporting service 68G may receive query requests for the event data 74A from client applications within historical time frames, such as a user may view PPE event information for a period of time and/or a computing device may analyze PPE event information for a period of time.

In an example implementation, the services 68 may also include a security service 68H that authenticates and authorizes users and requests using the ppmms 6. In particular, the security service 68H may receive authentication requests from client applications and/or other services 68 to access data in the data layer 72 and/or perform processing in the application layer 66. The authentication request may include credentials such as a username and password. Security service 68H may query security data 74A to determine whether the username and password combination is valid. The configuration data 74D may include security data in the form of authorization credentials, policies, and any other information used to control access to the ppmms 6. As described above, the security data 74A may include a combination of authorization credentials, such as a valid username and password for an authorized user of the ppmms 6. Other credentials may include a device identifier or device profile that allows access to the ppmms 6.

The security service 68H may provide auditing and logging functionality for operations performed at the ppmms 6. For example, security service 68H may record operations performed by service 68 and/or data in data layer 72 accessed by service 68. Security service 68H may store audit information such as logged operations, accessed data, and rule processing results in audit data 74C. In some examples, the security service 68H may generate an event in response to one or more rules being satisfied. Security service 68H may store data indicating these events in audit data 74C.

The PPEMS6 may include a self test component 68I, self test criteria 74E, and operational relationship data 74F. Self-test criteria 74E may include one or more self-test criteria. The work relationship data 74F may include a mapping between data corresponding to the PPE, the worker, and the work environment. The working relationship data 74F may be any suitable data storage area for storing, retrieving, updating, and deleting data. The RMRS 69G may store a mapping between the unique identifier of the worker 10A and the unique device identifier of the data hub 14A. The work relationship data store 74F may also map workers to environments. In the example of fig. 2, self-test component 68I may receive or otherwise determine data from work relationship data 74F for data hub 14A, worker 10A, and/or SRL11 associated with or assigned to worker 10A. Based on this data, self-test component 68I may select one or more self-test criteria from self-test criteria 74E. Self-test component 68I may send the self-test criteria to data hub 14A.

Fig. 3 shows an example of one of the SRLs 11 in more detail. In this example, the SRL11 includes a first connector 90 for attachment to an anchor, a lifeline 92, and a second connector 94 for attachment to a user (not shown). The SRL11 also includes a housing 96 that houses an energy absorption and/or braking system and a computing device 98. In the illustrated example, the computing device 98 includes a processor 100, a memory 102, a communication unit 104, one or more extension sensors 106, a tension sensor 108, an accelerometer 110, a position sensor 112, an altimeter 114, one or more environmental sensors 116, and an output unit 118.

It should be understood that the architecture and arrangement of the computing device 98 (and more broadly, the SRL 11) shown in fig. 3 is shown for exemplary purposes only. In other examples, SRL11 and computing device 98 may be configured in a variety of other ways with additional, fewer, or alternative components than those shown in fig. 3. For example, in some examples, computing device 98 may be configured to include only a subset of components, such as communication unit 104 and extension sensor(s) 106. Further, while the example of fig. 3 shows the computing device 98 as integrated with the housing 96, the techniques are not limited to such an arrangement.

The first connector 90 may be anchored to a fixed structure, such as a bracket or other support structure. The lifeline 92 may be wound around an offset drum that forms part of the rotator assembly and is rotatably connected to the housing 96. The second connector 94 may be connected to a user (e.g., such as one of the workers 10 (fig. 1)) via a lifeline 92. Thus, in some examples, the first connector 90 may be configured to connect to an anchor point of a support structure, and the second connector 94 is configured to include a hook to connect to a worker. In other examples, the second connector 94 may be connected to an anchor point, while the first connector 90 may be connected to a worker. When a user performs an activity, movement of the lifeline 92 causes the drum to rotate as the lifeline 92 extends out and retracts into the housing 96.

In general, computing device 98 may include one or more sensors that may capture real-time data regarding the operation of SRL11 and/or the environment in which SRL11 is used in real-time. Such data may be referred to herein as usage data. The sensor may be positioned within the housing 96 and/or may be positioned at other locations within the SRL11, such as adjacent to the first connector 90 or the second connector 94. In one example, the processor 100 is configured to implement functionality and/or process instructions for execution within the computing device 98. For example, the processor 100 may be capable of processing instructions stored by the memory 102. The processor 100 may include, for example: a microprocessor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or an equivalent discrete or integrated logic circuit.

Memory 102 may include a computer-readable storage medium or a computer-readable storage device. In some examples, the memory 102 may include one or more of short-term memory or long-term memory. The memory 102 may comprise, for example, forms of Random Access Memory (RAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), a magnetic hard disk, an optical disk, flash memory, or electrically programmable memory (EPROM) or electrically erasable and programmable memory (EEPROM).

In some examples, memory 102 may store an operating system (not shown) or other application programs that control the operation of components of computing device 98. For example, the operating system may facilitate communication of data from electronic sensors (e.g., extension sensors 106 such as magnetic sensors, tension sensors 108, accelerometers 110, position sensors 112, altimeters 114, and/or environmental sensors 116) to the communication unit 104. In some examples, memory 102 is used to store program instructions for execution by processor 100. The memory 102 may also be configured to store information within the computing device 98 during operation.

Computing device 98 may communicate with external devices via one or more wired or wireless connections using communication unit 104. The communication unit 104 may include various mixers, filters, amplifiers and other components designed for signal modulation, as well as one or more antennas and/or other components designed for transmitting and receiving data. Communication unit 104 may send data to and receive data from other computing devices using any one or more suitable data communication techniques. Examples of such communication technologies may include TCP/IP, Ethernet, Wi-Fi, Bluetooth, 4G, LTE (to name a few). In some cases, the communication unit 104 may operate according to a bluetooth low energy (BLU) protocol.

The extension sensor 106 may be configured to generate and output data indicative of at least one of extension of the lifeline 92 and retraction of the lifeline 92. In some examples, the extension sensor 106 may generate data indicative of at least one of an extended length of the lifeline 92 and a retracted length of the lifeline 92. In other examples, the extension sensor 106 may generate data indicative of an extension or retraction cycle. The extension sensor 106 may include one or more of a rotary encoder, an optical sensor, a magnetic sensor, or another sensor for determining position and/or rotation. Additionally, in some examples, the extension sensor 106 may also include one or more switches that generate an output indicative of full extension or full retraction of the lifeline 92. As described further below, in some examples, the extension sensor 106 may also include one or more magnetic sensors configured to measure changes in the magnetic field generated by rotation of the drum relative to the housing 96. The measured changes in the magnetic field may be used to determine the extension or retraction of the lifeline 92 and other useful information about the SRL 11. In some such examples, the extension sensor 106 may also function as a speedometer or accelerometer that provides data indicative of the speed or acceleration of the lifeline 92. For example, the extension sensor 106 may measure extension and/or retraction of the lifeline and apply the extension and/or retraction to a time scale (e.g., divided by time).

The tension sensor 108 may be configured to generate data indicative of the tension of the lifeline 92, for example, relative to the second connector 90. The tension sensor 108 may comprise a force sensor placed in-line with the lifeline 92 to directly or indirectly measure the tension applied to the SRL 11. In some cases, tension sensor 108 may include a strain gauge that measures a static force or static tension on SRL 11. Additionally or alternatively, the tension sensor 108 may include a mechanical switch having a spring-biased mechanism for establishing or breaking electrical contact based on a predetermined tension applied to the SRL 11. In other examples, the tension sensor 108 may include one or more components for determining rotation of a friction brake of the SRL 11. For example, one or more components may include a sensor (e.g., an optical sensor, a hall effect sensor, etc.) configured to determine relative motion between two components of the brake during activation of the braking system.

The accelerometer 110 may be configured to generate data indicative of acceleration of the SRL11 relative to gravity. The accelerometer 110 may be configured as a single-axis or multi-axis accelerometer to determine the magnitude and direction of acceleration, e.g., as a vector, and may be used to determine orientation, coordinates, acceleration, vibration, shock, and/or fall. In other examples, the acceleration of the SRL11 may be monitored by one of the other sensors (e.g., the extension sensor 106).

The location sensor 112 may be configured to generate data indicative of a location of the SRL11 in one of the environments 8. The location sensors 112 may include Global Positioning System (GPS) receivers, component parts that perform triangulation (e.g., using beacons and/or other fixed communication points), or other sensors for determining the relative location of the SRL 11.

Altimeter 114 may be configured to generate data indicative of the height of SRL11 above a fixed level. In some examples, altimeter 114 may be configured to determine the altitude of SRL11 based on measurements of atmospheric pressure (e.g., the greater the altitude, the lower the pressure).

The environmental sensor 116 may be configured to generate data indicative of characteristics of an environment, such as the environment 8. In some examples, the environmental sensors 116 may include one or more sensors configured to measure temperature, humidity, particulate content, noise level, air quality, or any kind of other characteristic of the environment in which the SRL11 may be used.

The output unit 118 may be configured to output data indicative of the operation of the SRL11, e.g., as measured by one or more sensors of the SRL11 (e.g., such as the extension sensor 106, the tension sensor 108, the accelerometer 110, the position sensor 112, the altimeter 114, and/or the environmental sensor 116). The output unit 118 may include instructions executable by the processor 100 of the computing device 98 to generate data associated with the operation of the SRL 11. In some examples, the output unit 118 may directly output data from one or more sensors of the SRL 11. For example, the output unit 118 may generate one or more messages containing real-time or near real-time data from one or more sensors of the SRL11 for transmission to another device via the communication unit 104.

In other examples, the output unit 118 (and/or the processor 100) may process data from one or more sensors and generate messages characterizing the data from the one or more sensors. For example, the output unit 118 may determine the length of time the SRL11 is in use, the number of extension and retraction cycles of the lifeline 92 (e.g., based on data from the extension sensor 106), the average rate of change of the user's speed during use (e.g., based on data from the extension sensor 106 or the position sensor 112), the instantaneous speed or acceleration of the user of the SRL11 (e.g., based on data from the accelerometer 110), the number of locks on the brake of the lifeline 92, and/or the severity of the impact (e.g., based on data from the tension sensor 108).

In some examples, output unit 118 may be configured to transmit the usage data to another device (e.g., PPE 62) via communication unit 104 in real-time or near real-time. However, in some instances, communication unit 104 may not be able to communicate with such devices, for example, due to an environment and/or network disruption in which SRL11 is located. In such examples, output unit 118 may cache the usage data to memory 102. That is, the output unit 118 (or the sensor itself) may store usage data to the memory 102, which may allow the usage data to be uploaded to another device when a network connection becomes available.

The output unit 118 may also be configured to generate audible, visual, tactile, or other output that may be perceived by a user of the SRL 11. For example, the output unit 118 may include one or more user interface devices including, for example, various lights, displays, tactile feedback generators, speakers, and the like. In one example, output unit 118 may include one or more Light Emitting Diodes (LEDs) located on SRL11 and/or included in a remote device (e.g., indicator glasses, goggles, etc.) located in the field of view of a user of SRL 11. In another example, output unit 118 may include one or more speakers located on SRL11 and/or included in a remote device (e.g., an earpiece, headset, etc.). In another example, the output unit 118 may include a tactile feedback generator that generates vibrations or other tactile feedback and is included on the SRL11 or a remote device (e.g., a bracelet, helmet, earpiece, etc.).

The output unit 118 may be configured to generate an output based on the operation of the SRL 11. For example, the output unit 118 may be configured to generate an output indicative of the status of the SRL11 (e.g., an output that the SRL11 is operating properly or requires inspection, repair, or replacement). As another example, the output unit 118 may be configured to generate an output indicating that the SRL11 is applicable to the environment in which the SRL11 is located. In some examples, the output unit 118 may be configured to generate output data indicating that the environment in which the SRL11 is located is unsafe (e.g., temperature, particle level, location, etc. are potentially dangerous to workers using the SRL 11).

In some examples, the SRL11 may be configured to store rules characterizing the likelihood of a security event, and the output unit 118 may be configured to generate an output based on a comparison of the operation of the SRL11 (as measured by the sensors) to the rules. For example, SRL11 may be configured to store rules to memory 102 based on the above-described model and/or data history from ppmms 6. Storing and enforcing the rules locally may allow SRL11 to determine the likelihood of a security event with potentially less delay than if such a determination was made by the ppmms 6 and/or if there is no available network connectivity (making communication with the ppmms 6 impossible). In this example, output unit 118 may be configured to generate audible, visual, tactile, or other output that alerts workers using SRL11 of potentially unsafe activities, abnormal behavior, and so forth.

According to aspects of the present disclosure, SRL11 may receive alert data via communication unit 104, and output unit 118 may generate an output based on the alert data. For example, the SRL11 may receive alert data from one of the hubs 14, the ppmms 6 (either directly or via one of the hubs 14), the end user computing device 16, a remote user using the computing device 18, the security station 15, or other computing device. In some examples, the alert data may be based on the operation of SRL 11. For example, the output unit 118 may receive alert data indicating the status of the SRL11, that the SRL11 is suitable for the environment in which the SRL11 is located, that the environment in which the SRL11 is located is unsafe, and so forth.

Additionally or alternatively, the SRL11 may receive alert data associated with a likelihood of a security event. For example, as described above, in some examples, the ppmms 6 may apply historical data and models to the usage data from the SRLs 11 to compute assertions, such as the occurrence of abnormal or predicted imminent safety events based on environmental conditions or behavioral patterns of workers using the SRLs 11. That is, the ppmms 6 may apply analysis to identify relationships or correlations between sensed data from the SRLs 11, environmental conditions of the environment in which the SRLs 11 are located, geographic areas in which the SRLs 11 are located, and/or other factors. The ppmms 6 may determine, based on data obtained throughout the worker population 10, which particular activities that may be within a certain environment or geographic area result in or predict the occurrence of an abnormally high safety event. The SRL11 may receive alert data from the ppmms 6 indicating a relatively high likelihood of a security event.

The output unit 118 can interpret the alert data and generate an output (e.g., an audible, visual, or tactile output) to notify workers using the SRL11 of the alert condition (e.g., a relatively high likelihood of a safety event, an environmental hazard, a failure of the SRL11, a need for repair or replacement of one or more components of the SRL11, etc.). In some cases, additionally or alternatively, the output unit 118 (or the processor 100) may interpret the alert data to modify the operation of the SRL11 or to enforce the rules of the SRL11 in order to conform the operation of the SRL11 to desired/less dangerous behavior. For example, the output unit 118 (or the processor 100) may actuate a brake on the lifeline 92 to prevent the lifeline 92 from extending from the housing 96.

Thus, data from sensors of the SRL11 (e.g., data from the extension sensor(s) 106, the tension sensor 108, the accelerometer 110, the position sensor 112, the altimeter 114, the environmental sensor 116, or other sensors) may be used in various ways in accordance with aspects of the present disclosure. According to some aspects, the usage data may be used to determine usage statistics. For example, the ppmms 6 may determine, based on usage data from the sensors: the amount of time the SRL11 is in use, the number of cycles of extension or retraction of the lifeline 92, the average rate of change of the speed at which the lifeline 92 is extended or retracted during use, the instantaneous speed or acceleration at which the lifeline 92 is extended or retracted during use, the number of locks on the lifeline 92, the severity of impact to the lifeline 92, and so forth. In other examples, the above-described usage statistics may be determined and stored locally (e.g., by one of the SRL11 or the hub 14).

According to aspects of the present disclosure, the PPEMS6 may use the usage data to characterize the activity of the worker 10. For example, the PPEMS6 may establish patterns of production and non-production times (e.g., based on operation of the SRL11 and/or movement of the worker 10), classify movement of the worker, identify key motions, and/or infer occurrence of key events. That is, the PPEMS6 may obtain usage data, analyze the usage data (e.g., by comparing the usage data to data from known activities/events) using the service 68, and generate an output based on the analysis.

In some examples, usage statistics may be used to determine when the SRL11 requires maintenance or replacement. For example, the PPEMS6 may compare the usage data to data of the SRL11 indicating normal operation to identify defects or anomalies. In other examples, the ppmms 6 may also compare the usage data to data indicative of known lifetime statistics for the SRL 11. Usage statistics may also be used to let product developers know the way in which SRLs 11 are used by workers 10 in order to improve product design and performance. In other examples, usage statistics may be used to collect human performance metadata to develop product specifications. In other examples, usage statistics may be used as a competitive benchmark tool. For example, usage data may be compared among customers of the SRL11 to evaluate metrics (e.g., productivity, compliance, etc.) across a population of workers equipped with the SRL 11.

Additionally or alternatively, usage data from sensors of the SRL11 may be used to determine the status indication according to aspects of the present disclosure. For example, the ppmms 6 may determine that the worker 10 is connected to or disconnected from the SRL 11. The PPEMS6 may also determine the height and/or position of the worker 10 relative to a reference. The ppmms 6 may also determine that the worker 10 is approaching a predetermined draw length of the lifeline 92. The PPEMS6 may also determine the proximity of the worker 10 to a hazardous area in one of the environments 8 (FIG. 1). In some cases, the ppmms 6 may determine the maintenance interval for the SRL11 based on the usage of the SRL11 (as indicated by the usage data) and/or the environmental conditions of the environment in which the SRL11 is located. The ppmms 6 may also determine whether SRLs 11 are connected to the anchor/fixed structure and/or whether the anchor/fixed structure is appropriate based on the usage data.

Additionally or alternatively, according to aspects of the present disclosure, usage data from sensors of the SRL11 may be used to assess the performance of the worker 10 wearing the SRL 11. For example, the ppmms 6 may identify a motion based on usage data from the SRL11 that may indicate that the worker 10 is about to fall. The PPEMS6 may also identify motions that may indicate fatigue based on usage data from the SRL 11. In some cases, the ppmms 6 may infer that a fall has occurred or that the worker 10 has no mobility based on usage data from the SRL 11. After a drop has occurred, the ppmms 6 may also perform drop data analysis and/or determine temperature, humidity, and other environmental conditions as they relate to the likelihood of a security event.

Additionally or alternatively, according to aspects of the present disclosure, usage data from sensors of the SRL11 may be used to determine alerts and/or actively control operation of the SRL 11. For example, the PPEMS6 may determine that a safety event such as a fall is imminent and activate the brakes of the SRL 11. In some cases, the ppmms 6 may dynamically adjust the stop characteristics according to the fall. That is, the ppmms 6 may alert the controls applied to the SRL11 based on the particular characteristics of the security event (e.g., as indicated by the usage data). In some examples, the ppmms 6 may provide a warning when the worker 10 approaches a hazard in one of the environments 8 (e.g., based on location data collected from the location sensors 112). The PPEMS6 may also lock the SRL11 so that the SRL11 will not operate after the SRL11 has experienced a bump or otherwise needed service.

Likewise, the ppmms 6 may determine the performance characteristics described above and/or generate alert data based on the application of the usage data to one or more security models that characterize the activities of the users of the SRLs 11. The security model may be trained based on historical data or known security events. However, while these determinations are described with respect to the ppmms 6, one or more other computing devices, such as the hub 14 or SRL11, may be configured to perform all or a subset of such functionality, as described in greater detail herein.

In some examples, the PPEMS6 may apply the analysis against a combination of PPEs. For example, the PPEMS6 may map the relevance between users of SRL11 and/or other PPEs used with SRL 11. That is, in some cases, the PPEMS6 may determine the likelihood of a security event based not only on usage data from the SRL11, but also from usage data from other PPEs used with the SRL 11. In such cases, the ppmms 6 may include one or more security models constructed from data from known security events from one or more devices used with the SRL11 other than the SRL 11.

In some examples, the functionality of the extension sensor 106 and/or the accelerometer 110 may be implemented by one or more magnetic sensors positioned within the SRL housing 96 to monitor the relative rotation of a rotator assembly (e.g., a drum) to which the lifeline 92 is connected. Fig. 4 shows an example of the internal components of an example SRL120 contained in a housing 122, the housing 122 including at least one such magnetic sensor. The SRL120 may serve as one or more SRLs 11 that form part of the ppmms 6.

In the illustrated example, the SRL120 includes a drum 124 rotatable about an axis 126, the axis 126 being connected to the housing 122. Lifeline 128 is attached to drum 124 and is coiled around drum 124, and may extend or retract based on the rotation of drum 124. The SRL120 also includes a rotor assembly 130 rotatably connected to the shaft 126, which includes a disc 132 and a drum 124. In some examples, the disc 132 is connected to the drum 124 such that when the lifeline 128 is extended or retracted, the disc 132 rotates with the drum 124.

As described further below, the disk 132 includes at least one region of ferromagnetic material 134. The SRL120 also includes at least one magnetic sensor 136 and magnet 138, each positioned adjacent the disc 132 at a fixed location relative to the housing 122 such that both the magnetic sensor 136 and the magnet 138 remain stationary within the housing 122 while the drum 124 and the disc 132 rotate about the shaft 126 as the lifeline 128 is extended or retracted. In some examples, disk 132 may also include one or more non-ferromagnetic regions 135 separating one or more regions of ferromagnetic material 134.

During operation, the magnetic sensor 136 measures the magnetic field generated by the magnet 138. As the lifeline 128 is extended or retracted, the disc 132 rotates within the SRL housing 122 such that at least one area of ferromagnetic material 134 is in close proximity to the magnet 138 and/or magnetic sensor 136. As used herein, a portion of the disk 132 that is "proximate" to the magnet 138 and/or the magnetic sensor 136 is used to describe a portion of the disk 132 that is radially aligned with the magnet 138 and/or the magnetic sensor 136, where radially aligned refers to a radius of the disk 132. For example, line 139 of fig. 4 shows the radial axis of the disk 132, which may be considered to be immediately adjacent to or radially aligned with the magnet 138 and the magnetic sensor 136. In some examples, the magnet 138 and the magnetic sensor 136 may each be radially aligned along a line 139. However, in other examples, the magnet 138 and the magnetic sensor 136 may be slightly offset from each other along the line 139 without disrupting the operation of the SRL120 or the detection of the ferromagnetic material 134 regions by the magnetic sensor 136 as the disk 132 rotates, and the respective ferromagnetic material 134 regions are in close proximity to the magnet 138 and/or the magnetic sensor 136.

When in close proximity to the magnet 138, the ferromagnetic material 134 will interrupt the magnetic field generated by the magnet 138. For example, fig. 5A and 5B illustrate the interruption of magnetic field lines 140 generated by the magnet 138 when a region of ferromagnetic material 134 is brought into close proximity to the magnet 138. Fig. 5A shows normal magnetic field lines 140 generated by the magnet 138 when the ferromagnetic material 134 is not in close proximity to the magnet 138. Such a configuration may be represented by the SRL120 when the non-ferromagnetic region 135 is positioned adjacent to the magnet 138. Fig. 5B illustrates how the magnetic field lines 140 generated by the magnet 138 may be interrupted when a region of ferromagnetic material 134 is positioned adjacent to the magnet 138 and in close proximity to the magnet 138.

As the disk 132 rotates, the interruption in the magnetic field lines 140 may create a measurable difference in the magnetic field, which may be measured by the magnetic sensor 136. The magnetic sensor 136 may be calibrated to detect measurable disturbances in the magnetic field as one or more regions of ferromagnetic material 134 rotate past the magnet 138 and magnetic sensor 136 to provide valuable usage data regarding the rotation of the disk 132 and drum 124. For example, the magnetic sensor 136 effectively monitors the rotation of the disk 132 within the SRL120 by detecting disturbances caused when one or more regions of ferromagnetic material 134 are in close proximity to the magnet 138 and/or the magnetic sensor 136. Such monitoring of the disc 132 may be analyzed by the computing device 98 to provide valuable usage data regarding the SRL120, including, for example, the number, degree, or angle of rotation of the disc 132 that may be associated with extension or retraction of the lifeline 128, the rotational speed of the disc 132 that may be associated with the speed at which the lifeline 128 is extended or retracted, the rotational acceleration of the disc 132 that may be associated with the acceleration at which the lifeline 128 is extended or retracted (e.g., such as in a fall of the worker 10), and so forth.

In some examples, the magnetic sensor 136 may be configured to function as a digital sensor that provides an indication when one or more regions of ferromagnetic material 134 are in close proximity to a region of magnet 138. Depending on the total number of regions of ferromagnetic material 134 disposed around the disk 132 and the frequency at which the regions of ferromagnetic material 134 pass through the magnet 138, the magnetic sensor 136 may provide useful information about the speed or acceleration at which the plate 132 rotates. For example, when the disk 132 includes only a single region of ferromagnetic material, each change in the magnetic field generated by the magnet 138 may represent one turn of the disk 132 and/or the drum 124. The presence of more regions of ferromagnetic material 134 on disk 132 may allow for greater resolution, precision, and/or accuracy in the measured parameter with respect to the number of revolutions of disk 132. In some examples, the disk 132 may include at least two regions of ferromagnetic material 134 that may be independently detected by the magnetic sensor 136 when the disk 132 rotates. The regions of ferromagnetic material 134 may be uniformly displaced around the disk 132 such that each successive region of ferromagnetic material 134 represents a set angle or rotation of the disk 132. In addition, uniform displacement of the regions of ferromagnetic material 134 will ensure balanced rotation of the disk 132.

In some examples, one or more regions of ferromagnetic material 134 may include one or more soft magnetic materials. As used herein, "soft magnetic material" is used to refer to a material that is magnetized when proximate to a magnetic field, but does not retain magnetization when distant from the magnetization field. Examples of suitable soft magnetic materials that may be included in the region of ferromagnetic material 134 may include, but are not limited to, iron or iron alloys (e.g., iron-silicon alloys, nickel-iron alloys), soft ferrites, cobalt or cobalt alloys, nickel or nickel alloys, gadolinium or gadolinium alloys, dysprosium and dysprosium alloys, or combinations thereof. Additionally or alternatively, the soft magnetic material may comprise a material having a roughness of less than 1000A/m and/or a relative permeability of greater than about 10. In some examples, the region of ferromagnetic material 134 can consist of or consist essentially of a soft magnetic material.

Magnet 138 may include one or more hard magnetic materials. As used herein, "hard magnetic material" is used to refer to a material that can be easily magnetized and will remain magnetized when moved away from an external magnetic field. In some examples, the hard magnetic material may be referred to as a permanent magnet. Examples of suitable hard magnetic materials may include, but are not limited to, alnico (e.g., nickel/cobalt/iron/aluminum alloy), hard ferrites, rare earth magnets, neodymium iron boron alloys, and samarium cobalt alloys, ceramic magnets. Additionally or alternatively, the hard magnetic material may comprise a material having a roughness of greater than 10,000A/m and/or a remanent magnetic field of 500 gauss or greater. In some examples, magnet 138 may consist of or consist essentially of a hard magnetic material.

In some examples, constructing the region(s) of ferromagnetic material 134 with a soft magnetic material and constructing the magnet 138 with a hard magnetic material may provide one or more manufacturing advantages in constructing the SRL 120. For example, in an alternative design of the SRL120, a disk 132 having a plurality of magnets (e.g., hard magnetic material) distributed around the circumference of the disk 132 may be included and the presence of the magnet 138 is excluded. As the disk rotates, each magnet is in close proximity to the magnetic sensor 136 to provide a detectable change in the magnetic field measured by the magnetic sensor 136 that is indicative of the rotation of the disk 132. In such examples, the accuracy with which the system can measure the degree of rotation of the disk 132 will directly correspond to the total number of magnets included on the disk 132. However, hard magnetic materials are generally more expensive than soft magnetic materials. Therefore, as measurement accuracy improves, including more magnets on the disk 132 will generally increase production costs. In contrast, by configuring disk 132 to include multiple regions of ferromagnetic material 134, accuracy of the angle of rotation of disk 132 may be obtained even if only one magnet 138 (e.g., hard magnetic material) is used to detect rotation of disk 132, thereby providing reduced production costs.

The magnetic sensor 136 may include any suitable sensor capable of detecting changes in a magnetic field. In some examples, the magnetic sensor 136 may include a transducer that provides a variable voltage output in response to a changing magnetic field. Example magnetic sensors 136 may include, for example, hall effect sensors, micro-electromechanical system (MEMS) magnetic sensors, Giant Magnetoresistance (GMR) sensors, Anisotropic Magnetoresistance (AMR), and the like.

As used herein, the one or more regions of ferromagnetic material 134 and the one or more non-ferromagnetic regions 135 are used to differentiate between the immediate vicinity of the disk 132 and portions adjacent to the magnet 138 and/or magnetic sensor 136 as the disk 132 rotates. As described further below, in some examples, the non-ferromagnetic region 135 can include a region of void space, such as a cut, a groove, a pit, a hole, a slot, or the like, separating the regions of ferromagnetic material 134. When the non-ferromagnetic region 135 is in close proximity to the magnet 138, the non-ferromagnetic region 135 will cause a measurable change in the magnetic field generated by the magnet 138, as compared to when the region of ferromagnetic material 134 is in close proximity to the magnet 138.

In examples where the non-ferromagnetic region 135 may comprise a void space region, the disk 132 may comprise any suitable material for its construction. For example, in some such examples, a disk 132 including one or more regions of ferromagnetic material 134 may be constructed using ferromagnetic material. When the associated non-ferromagnetic regions 135 (e.g., void spaces) are positioned proximate to the magnet 138 and/or the magnetic sensor 136, sufficient separation from the magnet 138 and/or the magnetic sensor 136 may be provided such that the body of the disk 132 does not affect the magnetic field generated by the magnet 132 or at least provides a measurable change in the magnetic field compared to when the regions of ferromagnetic material 134 are proximate to the magnet 138 and/or the magnetic sensor 136.

In other examples, the body of the disk 132 may include one or more non-ferromagnetic materials, with one or more regions of ferromagnetic material 134 attached to the disk 132. Examples of suitable non-ferromagnetic materials for constructing portions of the disk 132 may include, for example, composites, non-magnetic metals such as steel, aluminum, zinc, titanium, alloys thereof, 304 stainless steel, polymers, copper, and the like. In such examples, the non-ferromagnetic region 135 may comprise a void space region, or may comprise a body portion of the disk 132 constructed from a non-ferromagnetic material.

In some examples, the one or more regions of ferromagnetic material 134 may represent protrusions or castellations extending from the disk 132, and the one or more non-ferromagnetic regions 135 may represent portions or void spaces of non-magnetic material (e.g., cutouts within the disk 132). For example, the regions of ferromagnetic material 134 and non-ferromagnetic material 135 can be characterized as a series of one or more castellations along the perimeter of the disk 132. In such examples, the castellations represent regions of ferromagnetic material 134, while the cuts defining the castellations represent non-ferromagnetic regions 135 (e.g., regions lacking ferromagnetic material 134). In some such examples, the disc 132 may comprise a disc configured to be made of a single ferromagnetic material (e.g., iron) with a cut-out formed along an outer circumference of the disc 132 to define the non-ferromagnetic region 135. Each cut in turn defines a castellation that constitutes a region of ferromagnetic material 134.

In some examples, the regions of ferromagnetic material 134 can be arranged in a repeating pattern around the perimeter with each castellation (e.g., region of ferromagnetic material 134) being sufficiently separated from adjacent castellations by non-ferromagnetic regions 135 such that the magnetic sensor 136 is able to detect and distinguish each region of ferromagnetic material 134 from each non-ferromagnetic region 135 when the respective region is in close proximity to the magnet 138 as the disk 132 is rotated about the axis 126.

In examples including multiple regions of ferromagnetic material 134, each region of ferromagnetic material 134 may be at a distance (S) from an adjacent region of ferromagnetic material 134_d) (e.g., the distance of each non-ferromagnetic material 135) are evenly distributed. Separation distance (S)_d) Is largeThe size should be large enough to allow the magnetic sensor 136 to measurably distinguish each region of ferromagnetic material 134 as the disk 132 rotates about the axis 126. As described above, having more regions of ferromagnetic material 134 on disk 132 may improve the accuracy of determining: an extended/retracted length of lifeline 128, a degree or rotation of disc 132, an extension/retraction speed of lifeline 128, an extension/retraction acceleration of lifeline 128, a fall event, or a combination thereof. As a non-limiting example, a suitable separation distance (S) for a disc 132 defining a diameter of about 7.5cm rotating at a speed of about 900rpm_d) May be about 3 mm. In some examples, the ferromagnetic material 134 regions may have a minimum separation distance (S) of about 1mm_d-) In order to provide sufficient resolution of the region of ferromagnetic material 134 by the magnetic sensor 136.

Fig. 6-11 are schematic illustrations of example configurations in which the disc 132 may be constructed and arranged relative to the magnetic sensor 136 and the magnet 138. Each of the disk 132, magnet 138, and magnetic sensor 136 described in fig. 6-11 is incorporated into the SRL120 of fig. 4 as an alternative design and arrangement of the disk 132, magnetic sensor 136, and/or magnet 138, and may be described in connection with other components of the SRL 120.

Fig. 6 illustrates an example disc 132A that includes at least one region of ferromagnetic material 134A and at least one non-ferromagnetic region 135A that each are in close proximity to a magnet 138A as the disc 132A rotates about the axis 126. However, unlike the arrangement shown in FIG. 4, the magnetic sensor 136A and magnet 138A are aligned substantially parallel (e.g., parallel or nearly parallel) to the central axis of the magnetic disk 132A, with the magnetic sensor 136A and magnet 138A being located on opposite sides of the disk 132A. As the disk 132A rotates, each region of ferromagnetic material 134A and non-ferromagnetic material 135A will pass between the magnetic sensor 136A and the magnet 138A to cause a measurable change in the magnetic field generated by the magnet 138A. As with the example of fig. 4, both the magnetic sensor 136A and the magnet 138A may remain stationary in the SRL120 relative to the SRL housing 122.

Fig. 7 illustrates an example disc 132B that includes at least one region of ferromagnetic material 134B and at least one non-ferromagnetic region 135B that are each in close proximity to a magnet 138B as the disc 132B rotates about the axis 126. In the example shown in fig. 7, each region of ferromagnetic material 134B may be characterized as a protrusion extending from major surface 133B of disk 132B. The protrusions may take any suitable shape or size. Each of the protrusions of ferromagnetic material 134B shown in fig. 7 extends relative to the axial direction of disk 132B (e.g., parallel to the central axis of disk 132B). The one or more non-ferromagnetic regions 135B may be characterized as portions of the surface 133B of the disk 132B that do not include such protrusions or include ferromagnetic material. As the disk 132B rotates, each region of ferromagnetic material 134B will pass over the magnet 138B to cause a measurable change in the magnetic field generated by the magnet 138B, which can be detected by the magnetic sensor 136B. In some examples, the magnet 138B may be positioned between the magnetic sensor 136B and the passing region of the ferromagnetic material 134B. However, in other examples, the magnet 138B may be positioned such that each region of the ferromagnetic material 134B will pass between the magnetic sensor 136B and the magnet 138B as the disk 132B rotates about the axis 126. As with the previously described examples, both the magnetic sensor 136B and the magnet 138B may remain stationary in the SRL120 relative to the SRL housing 122.

In some examples, the regions of ferromagnetic material may be formed as distinct regions of ferromagnetic material inlaid into the surface of disk 132. For example, fig. 8 shows an example disc 132C that includes at least one region of ferromagnetic material 134C and at least one region of non-ferromagnetic material 135C that are each in close proximity to a magnet 138C as the disc 132C rotates about the axis 126. To form distinct regions of ferromagnetic material 134C and non-ferromagnetic material 135C, disk 132C may be constructed of a non-ferromagnetic material having one or more grooves defined in a major surface 133C of disk 132C. One or more grooves may then be inlaid with ferromagnetic material to form one or more regions of ferromagnetic material 134C, wherein the disk body forms non-ferromagnetic regions 135A separating the different regions of ferromagnetic material 134C. The ferromagnetic material 134C regions can have any suitable size or shape (e.g., square, rectangular, oval, circular, etc.) and can be present in any suitable amount. As the disk 132C rotates, each region of ferromagnetic material 134C will pass over the magnet 138C to cause a measurable change in the magnetic field generated by the magnet 138C, which can be detected by the magnetic sensor 136C. In some examples, the magnet 138C may be positioned between the magnetic sensor 136C and the passing region of the ferromagnetic material 134C. However, in other examples, the magnet 138C may be positioned such that each region of the ferromagnetic material 134C will pass between the magnetic sensor 136C and the magnet 138C as the disk 132C rotates about the axis 126. In such examples, the magnetic sensor 136C and the magnet 138C may be pre-positioned on opposite sides of the disk 132C. As with the previously described examples, the magnetic sensor 136C and the magnet 138C may both remain stationary in the SRL120 relative to the SRL housing 122.

Fig. 9A and 9B illustrate an example disc 132D that includes at least one region of ferromagnetic material 134D and at least one region of non-ferromagnetic 135D that are each in close proximity to a magnet 138D as the disc 132D rotates about the axis 126. Each of the one or more regions of ferromagnetic material 134D may be characterized as a protrusion on the surface 133D of the disk 132D that forms a castellation or track (e.g., protruding in a direction parallel to a central axis of the disk 132D) that protrudes axially from the surface 133D and extends in a generally radial direction through the surface 133D. However, other shapes, sizes, and patterns of protrusions of ferromagnetic material 134D may also be used.

In some examples, one or more non-ferromagnetic regions 135D can be characterized as grooves between protrusions of ferromagnetic material 134D, each of the grooves defining a side of an adjacent protrusion of ferromagnetic material 134D. In other examples, the grooves may be filled with a non-ferromagnetic material such that the disk 132D has a relatively smooth outer surface. As the disk 132D rotates, each region of ferromagnetic material 134D will pass over the magnet 138D to cause a measurable change in the magnetic field generated by the magnet 138D, which can be detected by the magnetic sensor 136D.

In some examples, as the disk 132D rotates about the axis 126, the magnet 138D may be positioned between the magnetic sensor 136D and the passing region of the ferromagnetic material 134D, as shown in the configuration of fig. 9A. In other examples, the magnet 138D may be positioned such that each region of the ferromagnetic material 134D will pass between the magnetic sensor 136D and the magnet 138D as the disk 132D rotates about the axis 126. Fig. 9B illustrates such a configuration, where magnet 138D is positioned adjacent to the surface of disk 132D opposite surface 133D. As with the previously described examples, both the magnetic sensor 136D and the magnet 138D may remain stationary in the SRL120 relative to the SRL housing 122.

In some examples, the magnetic sensor 136 and one or more regions of ferromagnetic material 134 may be configured to provide a measurable indication of the direction of rotation of the disc 132 (e.g., whether the disc 132 is rotating to extend or retract the lifeline 128). In some examples, a single magnet 138 and magnetic sensor 136 may be used to determine the direction of rotation of the disk 132 by configuring one or more regions of ferromagnetic material 134 to exactly adjust the magnetic field produced by the magnet 138 when the respective region passes the magnet 138. For example, one or more regions of ferromagnetic material 134 may include a gradient surface configured to vary as the modulation of the disk 132 due to the magnetic field generated by the magnet 138 as the gradient surface of the region of ferromagnetic material 134 rotates past the magnet 138. When paired with an analog magnetic sensor 136, an adjustment change (e.g., an increase or decrease change) in the magnetic field may provide an indication of the direction of rotation of the disk 132.

Fig. 10 is an exemplary disc 132E that may be incorporated in the SRL 120. The disk 132E includes at least one region of ferromagnetic material 134E that is proximate to the magnet 138E when the disk 132E is rotated about the axis 126. Each of the one or more regions of ferromagnetic material 134E may be characterized as a protrusion extending radially from the disk 132E. Each protrusion of ferromagnetic material 134E may define a slanted or saw tooth pattern having a tapered surface 144E that adjusts the distance between the respective region of ferromagnetic material 134E and magnet 138E as region 134E rotates in close proximity to magnet 138E. For example, the protrusion of the ferromagnetic material 134E may include a first end 146E and a second end 148E that define a leading edge (e.g., an apex) and a trailing edge, respectively, of a slanted or sawtooth pattern. As the disk 132E rotates in the clockwise direction 150, the first end 146E (e.g., leading edge) of the regional ferromagnetic material 134E is proximate to the magnet 138E (e.g., radially aligned). Due to the relatively short separation distance between first end 146E and magnet 138E, first end 146E will produce the greatest disruption in the magnetic field generated by magnet 138E. As the disk 132E continues to rotate in the clockwise direction 150, the separation distance between the magnet 138E and the region of ferromagnetic material 134E will gradually increase as a portion of the tapered surface 144E is proximate to the magnet 138E (e.g., radially aligned). The increasing separation distance will gradually reduce the interruption of the magnetic field caused by the region of ferromagnetic material 134E until second end 148E is proximate to magnet 138E (e.g., radially aligned). Thus, the magnetic sensor 136E may measure a large initial peak in the change in the magnetic field generated by the magnet 138E, which then gradually decreases back to the baseline value. In contrast, where the disk 132E is rotating in a counterclockwise direction, the magnetic sensor 136E may measure a gradual change in the magnetic field generated by the magnet 138E, which then abruptly changes back to a baseline value. The computing device 98 may be configured to correlate such changes in the signal detected by the magnetic sensor 136E with either clockwise or counterclockwise rotation of the disk 132E.

In some examples, disk 132E may include one or more non-ferromagnetic regions 135E separating each region of ferromagnetic material 134E. In other examples, one or more non-ferromagnetic regions 135E may be excluded from the disk 132E due to the tuned design of the regions of ferromagnetic material 134E. For example, the perimeter of the disk 132E may include only one or more regions of ferromagnetic material 134E, each region defining a slanted or sawtooth pattern. In such examples, the second end 148E may be radially aligned with the first end 146E (e.g., in examples where only one sloped or indented region of the ferromagnetic material 134E is present), or may be radially aligned with the first end of an adjacent region of the ferromagnetic material 134E.

Although the disk 132E is shown and described with the tapered surface 144E of one or more protrusions having a decreasing gradient relative to the disk 132E rotating in the clockwise direction 150, in other examples, the slanted or saw tooth pattern of the protruding regions of the ferromagnetic material 134E may be counter-rotating such that the tapered surface 144E of one or more protrusions has an increasing gradient relative to the disk 132E rotating in the clockwise direction 150. Additionally, as with the previously described examples, both the magnetic sensor 136E and the magnet 138E may remain stationary in the SRL120 relative to the SRL housing 122.

Fig. 11A and 11B are another example of a disc 132F that may be incorporated in the SRL120, the SRL120 being configured to provide a measurable indication of the direction of rotation of the disc 132F. Fig. 11A is a perspective view of the disc 132F, and fig. 11B is a cross-sectional view of the disc 132F along line a-a.

The disk 132F includes at least one region of ferromagnetic material 134F that is proximate to the magnet 138F when the disk 132F is rotated about the axis 126. Each of the one or more regions of ferromagnetic material 134F may be characterized as a protrusion extending axially from the surface 133F of the disk 132F. Each protrusion of ferromagnetic material 134F may define a slanted or saw tooth pattern having a tapered surface 144F that adjusts the distance between the respective region of ferromagnetic material 134F and magnet 138F as region 134F rotates in close proximity to magnet 138F. For example, the protrusions of ferromagnetic material 134F may include a first end 146F and a second end 148F that define a leading edge (e.g., an apex representing the greatest separation from surface 133F) and a trailing edge (e.g., flush with surface 133F) of the ramped or saw-tooth pattern of protrusions, respectively.

As the disk 132F rotates in the clockwise direction 150, the first end 146F (e.g., leading edge) of the regional ferromagnetic material 134F is proximate to the magnet 138F (e.g., radially aligned). Due to the relatively short separation distance between first end 146F and magnet 138F, first end 146F will produce the greatest disruption in the magnetic field generated by magnet 138F. As the disk 132F continues to rotate in the clockwise direction 150, the separation distance between the magnet 138F and the region of ferromagnetic material 134F will gradually increase as a portion of the tapered surface 144F is proximate to the magnet 138F (e.g., radially aligned). As described in the previous example, the increased separation distance will gradually reduce the interruption of the magnetic field caused by the region of ferromagnetic material 134F until the second end 148F is proximate to the magnet 138F (e.g., radially aligned). Thus, the magnetic sensor 136F may measure a large initial peak in the change in the magnetic field generated by the magnet 138F, which then gradually decreases back to the baseline value. In contrast, where the disk 132F is rotated in a counterclockwise direction, the magnetic sensor 136F may measure a gradual change in the magnetic field generated by the magnet 138F, which then abruptly changes back to a baseline value. The computing device 98 may be configured to correlate such changes in the signal detected by the magnetic sensor 136F with either clockwise or counterclockwise rotation of the disk 132F.

In some examples, disk 132F may include one or more non-ferromagnetic regions 135F separating each region of ferromagnetic material 134F. In other examples, one or more non-ferromagnetic regions 135F may be excluded from the disk 132F due to the tuned design of the regions of ferromagnetic material 134F. For example, the portion of the surface 133F that aligns with the magnetic sensor 138F as the disk 132E rotates may include only one or more regions of ferromagnetic material 134F, each region defining a slanted or sawtooth pattern. In such examples, the second end 148F may be radially aligned with the first end 146F (e.g., in examples where there is only one sloped or indented region of the ferromagnetic material 134F), or may be radially aligned with the first end of an adjacent region of the ferromagnetic material 134F.

Although the disk 132F is shown and described with the tapered surface 144F of one or more protrusions having a decreasing gradient relative to the disk 132F rotating in the clockwise direction 150, in other examples, the slanted or saw tooth pattern of the protruding regions of the ferromagnetic material 134F may be counter-rotating such that the tapered surface 144F of one or more protrusions has an increasing gradient relative to the disk 132F rotating in the clockwise direction 150. Additionally, as with the previously described examples, the magnetic sensor 136F and the magnet 138F may both remain stationary in the SRL120 relative to the SRL housing 122.

In other examples, the disk configuration described with respect to fig. 4 and 6-9 may be used to determine the rotational direction of the disk 132 by including a pair of magnetic sensors arranged in an orthogonal encoding configuration. Fig. 12 is an exemplary disc 132G that may be incorporated in the SRL 120. The disk 132G includes at least one region of ferromagnetic material 134G and first and second

magnetic sensors

136G and 136H each paired with respective first and

second magnets

138G and 138H. As the disk 132E rotates about the axis 126, each of the one or more regions of ferromagnetic material 134G will interrupt the magnetic field generated by the first magnet 138G or the second magnet 138H as the region of ferromagnetic material 134G is proximate the first magnet 138G and the second magnet 138H and/or the

magnetic sensors

136G and 136H. Each of the first and second

magnetic sensors

136G and 136H and the

respective magnets

138G and 138H may be arranged in any of the configurations described above, but will be positioned within the SRL housing 122 such that the first and second

magnetic sensors

136G and 136H differ from each other by approximately 90 degrees (e.g., a quadrature encoding configuration). For example, the SRL120 can be arranged such that when the center of the non-ferromagnetic region 135G is proximate to the location of the first magnet 138G and/or the first magnetic sensor 136G, the leading or trailing edge 148G of the region of ferromagnetic material 134G is proximate to the second magnet 138H and/or the second magnetic sensor 138H. Thus, in addition to the length, velocity, or acceleration sensing described above, the orthogonally encoded configuration of the pair of

magnetic sensors

136G and 136H can also readily determine the direction of rotation of the disk 132G.

Fig. 13 is a diagram illustrating an exemplary model applied by the personal protective equipment management system or other device herein to measure rope speed, acceleration, and rope length with respect to worker activity, wherein the model is arranged to define safe and unsafe zones. In other words, fig. 13 is a diagram representing a model applied by the ppmms 6, the hub 14, or the SRL11, 120 to predict the likelihood of a safety event based on measurements of the acceleration 160 of the lifeline (e.g., lifeline 128 shown in fig. 4) extraction or retraction, the speed 162 of the lifeline 128 extraction or retraction, and the length 164 of the lifeline that has been extracted or retracted. Measurements of acceleration 160, velocity 162, and length 164 may be determined based on data collected from sensors of the SRL120, such as the magnetic sensor 136. The data represented by the graph may be estimated or collected in a training/testing environment, and the graph may be used as a "map" that distinguishes safe activities from unsafe activities of a worker.

For example, the safety zone 166 may represent measurements of the acceleration 160, the velocity 162, and the length 164 associated with the safety activity (e.g., as determined by monitoring the activity of a worker in the test environment). The untethered area 168 may represent measurements of acceleration 160, velocity 162, and length 164 associated with a lifeline 128 that is not securely anchored to the support structure (which may be considered unsafe). The over-stretched region 170 may represent measurements of acceleration 160, velocity 162, and length 164 associated with the lifeline 128 extending beyond normal operating parameters (which may also be considered unsafe). Over-acceleration region 172 may represent measurements of acceleration 160, velocity 162, and length 164 associated with lifeline 128 extending rapidly beyond normal operating parameters (which may also indicate a user fall or unsafe use).

According to aspects of the present disclosure, the ppmms 6, the hub 14, or the SRL11, SRL120 may issue one or more alerts by applying the model or rule set shown in fig. 13 to the usage data received from the SRL11, SRL 120. For example, if the measurements of acceleration 160, velocity 162, or length 164 are outside of the safe region 166, the ppmms 6, hub 14, or

SRLs

11, 120 may issue an alert. In some cases, different alerts may be issued based on the extent to which measurements of acceleration 160, velocity 162, or length 164 are outside of safe region 166. For example, if measurements of acceleration 160, velocity 162, or length 164 are relatively close to a security region 166, the ppmms 6, hub 14, or SRL11, SRL120 may issue alerts that the activity is of interest and may result in a security event. In another example, if the measurements of acceleration 160, velocity 162, or length 164 are relatively far from the security zone 166, the ppmms 6, hub 14, or SRL11, SRL120 may issue an alert that the activity is unsafe and has a high likelihood of an impending security event.

In some cases, the data of the graph shown in FIG. 13 may represent historical data and model 74B shown in FIG. 2. In this example, the ppmms 6 may compare the incoming data stream to the map shown in fig. 13 to determine the likelihood of a security event. In other cases, similar maps may additionally or alternatively be stored to SRL11, SRL120, and/or hub 14, and alerts may be issued based on locally stored data.

While the example of FIG. 13 shows acceleration 160, velocity 162, and length 164, other maps may be developed with more or fewer variables than those shown. In one example, the map may be generated based solely on the length of the extended lifeline 128, as measured, for example, by the magnetic sensor 136. In this example, an alert may be issued to the worker when lifeline 128 extends beyond the line length specified by the map.

Fig. 14A and 14B are diagrams illustrating graphs of example input event data streams received and processed by the ppmms 6, the hub 14, or the SRL11, 120 and determined to represent low risk behavior (fig. 14A) and high risk behavior (fig. 14B) resulting in triggering an alert or other response based on application of one or more models or rule sets, in accordance with aspects of the present disclosure. In these examples, fig. 14A and 14B show graphs of example event data determined over a period of time to indicate safe activity and unsafe activity, respectively. For example, the example of fig. 14A shows a rate 190 at which a lifeline (such as lifeline 128 shown in fig. 4) is withdrawn or retracted relative to a kinematic threshold 192, while the example of fig. 14B shows a rate 194 at which a lifeline (such as lifeline 128 shown in fig. 4) is withdrawn relative to threshold 192.

In some examples, the graphs shown in fig. 14A and 14B may be developed and stored as historical data and model 74B of the PPEMS6 shown in fig. 2. According to aspects of the present disclosure, the ppmms 6, the hub 14, or the

SRLs

11, 120 may issue one or more alerts by comparing the usage data from the

SRLs

11, 120 to a threshold 192. For example, in the example of fig. 14B, the ppmms 6, the hub 14, or the

SRLs

11, 120 may issue one or more alerts when the speed 194 exceeds the threshold 192. In some cases, different alerts may be issued based on the extent to which the speed exceeds threshold 192, for example, in order to distinguish dangerous activity from activity that is unsafe and has a high likelihood of an impending safety event.

Fig. 15 is an example process for predicting a likelihood of a security event, according to aspects of the present disclosure. While the techniques shown in fig. 15 are described with respect to the ppmms 6, it should be understood that these techniques may be performed by various computing devices.

In the example shown, the ppmms 6 obtain usage data (200) from at least one self-retracting lifeline (SRL), such as at least one of the SRLs 120. As described herein, the usage data includes data indicative of the operation of the SRL 120. In some examples, the ppmms 6 may obtain the usage data by polling the SRL120 or the hub 14 to obtain the usage data. In other examples, the SRL120 or the hub 14 may send usage data to the ppmms 6. For example, the PPEMS6 may receive usage data from the PPEs 120 or hub 14 in real time as the usage data is generated. In other examples, the ppmms 6 may receive stored usage data.

In some examples, acquiring the usage data may include propagating the usage data by rotating the disc 132 of the SRL120 indicating extension or retraction of the lifeline 128, and monitoring the extent of the rotation or extension/retraction by measuring the interruption of the magnetic field generated by the magnet 138 using one or more magnetic sensors 136. As described above with respect to fig. 4, the magnet 138 and the magnetic sensor 136 may each be positioned in a fixed location within the SRL housing 122. The disc 132 may include one or more regions of ferromagnetic material 134 that are in close proximity to the magnet 138 and/or magnetic sensor 136 when the disc 132 is rotated about the shaft 126 with the lifeline 128 extended or retracted within the SRL housing 122. The magnet 138 and the magnetic sensor 136 may be positioned such that when each region of ferromagnetic material 134 is proximate to the magnet 138 and/or the magnetic sensor 136, the ferromagnetic material 134 regions modify the magnetic field generated by the magnet 138. The computing device 98 may be configured to measure changes in the magnetic field via the magnetic sensor 136 and calculate one or more of a number/angle of rotation of the disc 132, a rotational speed of the disc 132, a rotational acceleration of the disc 132, and a direction of rotation of the disc 132. The computing device 98 then converts such measurements into one or more of a length, velocity, or acceleration of the lifeline 128 based on physical parameters of the SRL120 (e.g., the size and diameter of the drum 124 around which the lifeline 128 is wound).

The ppmms 6 may apply the usage data to a security model that characterizes activities of a user of the at least one SRL120 (202). For example, as described herein, a security model may be trained based on data from known security events and/or historical data from the SRL 120. In this way, the security model may be arranged to define a secure area and an unsecure area.

The ppmms 6 may predict a likelihood of occurrence of a security event associated with the at least one SRL120 based on application of the usage data to the security learning model (204). For example, the ppmms 6 may apply the obtained usage data to a security model to determine whether the usage data conforms to a secure activity (e.g., as defined by the model) or conforms to a potentially unsecure activity.

The ppmms 6 may generate an output in response to predicting a likelihood of occurrence of a security event (206). For example, the ppmms 6 may generate alert data when the usage data does not comply with security activities (as defined by the security model). The ppmms 6 may send alert data indicating the likelihood of the occurrence of a security event to the SRL120, a security administrator, or another third party.

It will be recognized that, according to an example, some acts or events of any of the techniques described herein can be performed in a different order, added, combined, or left out altogether (e.g., not all described acts or events are necessary for the practice of the techniques). Further, in some examples, acts or events may be performed concurrently, e.g., through multi-threaded processing, interrupt processing, or multiple processors, rather than sequentially.

In one or more examples, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium and executed by a hardware-based processing unit. The computer readable medium may include a computer readable storage medium, which corresponds to a tangible medium, such as a data storage medium, or a communication medium, which includes any medium that facilitates transfer of a computer program from one place to another, such as according to a communication protocol. In this manner, the computer-readable medium may generally correspond to (1) a non-transitory tangible computer-readable storage medium or (2) a communication medium such as a signal or carrier wave. A data storage medium may be any available medium that can be accessed by one or more computers or one or more processors to retrieve instructions, code and/or data structures for implementing the techniques described in this disclosure. The computer program product may include a computer-readable medium.

By way of example, and not limitation, such computer-readable storage media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, flash memory, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer. Also, any connection is properly termed a computer-readable medium. For example, if instructions are transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium.

It should be understood, however, that computer-readable storage media and data storage media do not include connections, carrier waves, signals, or other transitory media, but are instead directed to non-transitory, tangible storage media. Disk and disc, as used herein, includes Compact Disc (CD), laser disc, optical disc, Digital Versatile Disc (DVD), floppy disk and blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

The instructions may be executed by one or more processors, such as one or more Digital Signal Processors (DSPs), general purpose microprocessors, Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs), or other equivalent integrated or discrete logic circuitry, as well as any combinations of such components. Thus, as used herein, the term "processor" may refer to any of the foregoing structure or any other structure suitable for implementing the techniques described herein. In addition, in some aspects, the functionality described herein may be provided within dedicated hardware and/or software modules. Furthermore, the techniques may be implemented entirely in one or more circuits or logic units.

The techniques of this disclosure may be implemented in a wide variety of devices or apparatuses including a wireless communication device or wireless handset, a microprocessor, an Integrated Circuit (IC), or a set of ICs (e.g., a chipset). Various components, modules, or units are described in this disclosure to emphasize functional aspects of devices configured to perform the disclosed techniques, but do not necessarily require realization by different hardware units. Rather, as noted above, various combinations of elements may be combined in hardware elements or provided by a collection of interoperative hardware elements including one or more processors as noted above, in conjunction with suitable software and/or firmware.

Various examples have been described. These examples, as well as others, are within the scope of the following claims.

Claims

1. A fall arrest device, comprising:

a device housing;

a shaft within the device housing;

a rotating body assembly rotatably connected to the shaft, the rotating body assembly comprising a disk and a drum, the disk comprising at least one region of ferromagnetic material;

an extendable lifeline connected to the drum and coiled thereon, the lifeline configured to connect the fall arrest device to a user or a support structure, wherein the extension of the lifeline causes the disk and the drum to rotate about the shaft;

a magnetic sensor positioned stationary relative to the device housing, the magnetic sensor positioned adjacent to the disk; and

a magnet comprising hard magnetic material, the magnet positioned stationary relative to the device housing and the magnetic sensor, wherein the magnetic sensor is configured to detect a change in a magnetic field generated by the magnet as the disk rotates about the axis, the change in the magnetic field being caused by the at least one region of ferromagnetic material being in close proximity to the magnet as the disk rotates.

2. The device of claim 1 wherein the disc comprises a plurality of regions of ferromagnetic material including the at least one region of ferromagnetic material, wherein each region of ferromagnetic material of the plurality of regions of ferromagnetic material causes the magnetic sensor to detect a change in magnetic field as the disc rotates.

3. The device of claim 2 wherein the disc comprises a plurality of non-ferromagnetic regions separating the plurality of regions of ferromagnetic material.

4. The device of claim 3 wherein the plurality of non-ferromagnetic regions are defined by cuts, slots, dimples, holes or grooves along the disc.

5. The device of claim 2 wherein the disc comprises a plurality of protrusions, wherein each protrusion forms one of the plurality of regions of ferromagnetic material.

6. The device of claim 5 wherein the plurality of protrusions comprise a plurality of castellations defined along the circumference of the disc such that each castellation extends radially outward from the disc.

7. The device of claim 5 wherein the plurality of protrusions comprise a plurality of castellations defined along a major surface of the disc such that each castellation extends axially outwardly from the major surface of the disc.

8. The device of claim 5 wherein the plurality of protrusions are positioned along a major surface of the disc such that each protrusion extends axially outward from the major surface of the disc.

9. The device of claim 5 wherein the plurality of protrusions extend radially outward from the disc along a circumference of the disc, each protrusion of the plurality of protrusions defining a saw-tooth shape.

10. The device of claim 1 wherein the magnetic sensor is configured to generate usage data about the device, the usage data comprising at least one of a rotation angle of the disc, a number of rotations of the disc, a rotational speed of the disc, or an acceleration of the disc.

11. The device of claim 10 further comprising a computing device configured to collect the usage data and a wireless transmitter configured to transmit the usage data corresponding to a fall arrest device.

12. The device of claim 11 wherein the wireless transmitter is configured to transmit a message to a mobile phone or control center in response to the rotational speed of the disc or the acceleration of the disc indicating a user fall.

13. The device of claim 1 wherein the magnetic sensor comprises an analog magnetic sensor and wherein the at least one region of ferromagnetic material is configured to exactly adjust the magnetic field generated by the magnet to produce a first change in the magnetic field when the at least one region of ferromagnetic material passes in close proximity to the magnet when the disc rotates in a clockwise direction and a second change in the magnetic field when the at least one region of ferromagnetic material passes in close proximity to the magnet when the disc rotates in a counterclockwise direction, the first and second changes in the magnetic field being different, the magnetic sensor configured to determine the direction of rotation of the disc based on the first and second changes in the magnetic field.

14. The device of claim 13 wherein the at least one region of ferromagnetic material defines a ramp or saw tooth shape.

15. The device of claim 1 further comprising:

a computing device configured to power the magnetic sensor and analyze signals generated by the magnetic sensor to produce usage data about the fall arrest device, the usage data including at least one of a rotation angle of the disc, a number of rotations of the disc, a rotational speed of the disc or an acceleration of the disc to detect a fall of a worker.

16. The device of claim 1 wherein the magnet is positioned between the magnetic sensor and the disc.

17. The device of claim 1 wherein the magnet and the magnetic sensor are positioned such that the at least one region of ferromagnetic material passes between the magnetic sensor and the magnet as the disc rotates.

18. The device of claim 1 wherein the magnet and the magnetic sensor are aligned along an axis substantially parallel to a radius of the disc.

19. The device of claim 1 wherein the magnet and the magnetic sensor are aligned along an axis substantially parallel to the axis of rotation of the disc.

20. The device of claim 1 wherein the at least one region of ferromagnetic material comprises a soft magnetic material.

21. The device of claim 20 wherein the magnet consists essentially of a hard magnet and the at least one region of ferromagnetic material consists essentially of the soft magnetic material.

22. The device of claim 20 wherein the magnetically soft material comprises at least one material selected from the list consisting of: iron, iron alloys, iron-silicon alloys, nickel-iron alloys, soft ferrites, cobalt alloys, nickel alloys, gadolinium alloys, dysprosium, and dysprosium alloys.

23. The device of claim 20 wherein the hard magnetic material comprises at least one material selected from the list consisting of: alnico, hard ferrite, rare earth magnets, neodymium iron boron alloy, samarium cobalt alloy, ceramic magnets.

24. A fall arrest device, comprising:

a device housing;

a shaft within the device housing;

a first magnetic sensor positioned stationary relative to the device housing, the first magnetic sensor positioned adjacent to the disk;

a first magnet comprising hard magnetic material, the first magnet positioned stationary relative to the device housing and the first magnetic sensor, wherein the first magnetic sensor is configured to detect a change in a first magnetic field generated by the first magnet as the disk rotates about the axis, the change in the first magnetic field being caused by the at least one region of ferromagnetic material being in close proximity to the first magnet as the disk rotates;

a second magnetic sensor positioned stationary relative to the device housing, the second magnetic sensor positioned adjacent to the disk; and

a second magnet comprising hard magnetic material, the second magnet positioned stationary with respect to the device housing and the second magnetic sensor, wherein the second magnetic sensor is configured to detect changes in a second magnetic field generated by the second magnet as the disk rotates about the axis, the changes in the second magnetic field being caused by the at least one region of ferromagnetic material being in close proximity to the second magnet as the disk rotates,

wherein the first and second magnetic sensors are positioned about 90 ° out of phase in an orthogonally encoded configuration, the first and second magnetic sensors configured to determine a direction of rotation of the disk based on the orthogonally encoded configuration.

25. The device of claim 24 wherein the disc comprises a plurality of regions of ferromagnetic material including the at least one region of ferromagnetic material, wherein each region of ferromagnetic material in the plurality of regions of ferromagnetic material causes the first and second magnetic sensors to detect changes in magnetic field as the disc rotates.

26. The device of claim 25 wherein the disc comprises a plurality of non-ferromagnetic regions separating the plurality of regions of ferromagnetic material.

27. The device of claim 26 wherein the plurality of non-ferromagnetic regions are defined by cuts, slots, dimples, holes or grooves along the disc.

28. The device of claim 25 wherein the disc comprises a plurality of protrusions, wherein each protrusion forms one of the plurality of regions of ferromagnetic material.

29. The device of claim 28 wherein the plurality of protrusions comprise a plurality of castellations defined along the circumference of the disc such that each castellation extends radially outward from the disc.

30. The device of claim 28 wherein the plurality of protrusions comprise a plurality of castellations defined along a major surface of the disc such that each castellation extends axially outwardly from the major surface of the disc.

31. The device of claim 28 wherein the plurality of protrusions are positioned along a major surface of the disc such that each protrusion extends axially outward from the major surface of the disc.

32. The device of claim 28 wherein the plurality of protrusions extend radially outward from the disc along a circumference of the disc, each protrusion of the plurality of protrusions defining a saw-tooth shape.

33. The device of claim 24 wherein at least one of the first and second magnetic sensors is configured to generate usage data about the device, the usage data including at least one of a rotation angle of the disc, a number of rotations of the disc, a rotational speed of the disc, or an acceleration of the disc.

34. The device of claim 33 further comprising a computing device configured to collect the usage data and a wireless transmitter configured to transmit the usage data corresponding to a fall arrest device.

35. The device of claim 34 wherein the wireless transmitter is configured to transmit a message to a mobile phone or control center in response to the rotational speed of the disc or the acceleration of the disc indicating a user fall.

36. The device of claim 24 further comprising:

a computing device configured to power the first and second magnetic sensors and analyze signals generated by the first and second magnetic sensors to produce usage data about the fall arrest device, the usage data including at least one of a rotation angle of the disc, a rotation direction of the disc, a number of revolutions of the disc, a rotation speed of the disc, or an acceleration of the disc to detect a fall of a worker.

37. The device of claim 24 wherein the at least one region of ferromagnetic material comprises a soft magnetic material.

38. The device of claim 37 wherein the first and second magnets consist essentially of the hard magnetic material and the at least one region of ferromagnetic material consists essentially of the soft magnetic material.

39. The device of claim 37 wherein the magnetically soft material comprises at least one material selected from the list consisting of: iron, iron alloys, iron-silicon alloys, nickel-iron alloys, soft ferrites, cobalt alloys, nickel alloys, gadolinium alloys, dysprosium, and dysprosium alloys.

40. The device of claim 37 wherein the hard magnetic material comprises at least one material selected from the list consisting of: alnico, hard ferrite, rare earth magnets, neodymium iron boron alloy, samarium cobalt alloy, ceramic magnets.

41. A method for obtaining data from a fall arrest device, the method comprising:

rotating a disc of the fall arrest device, wherein the fall arrest device comprises:

a device housing;

a shaft within the device housing;

a magnet comprising a hard magnetic material, the magnet positioned stationary with respect to the device housing and the magnetic sensor, wherein the magnetism generates a magnetic field, an

A processing circuit connected to the magnetic sensor;

measuring, with the processing circuitry, an interruption of the magnetic field generated by the magnet using the magnetic sensor, wherein the interruption of the magnetic field is generated by rotating the disk such that the at least one region of ferromagnetic material is in close proximity to the magnet or the magnetic sensor, thereby causing the magnetic sensor to measure a change in the magnetic field,

analyzing, with the processing circuit, the measured interruption of the magnetic field to determine at least one of a rotation angle of the disk, a number of rotations of the disk, a rotational speed of the disk, or a rotational acceleration of the disk.

42. The method of claim 41, wherein the disk comprises a plurality of regions of ferromagnetic material including the at least one region of ferromagnetic material, wherein the interruption of the magnetic field is generated after each region of ferromagnetic material in the plurality of regions of ferromagnetic material is rotated to be immediately adjacent to the magnet or the magnetic sensor as the disk rotates.

43. The method of claim 41, wherein the fall arrest device further comprises a wireless transmitter, the method further comprising:

analyzing, with the processing circuitry, the measured interruption of the magnetic field to detect the rotational speed of the disc or the rotational acceleration of the disc indicative of a user fall; and

using the processing circuitry, in response to detection of the user falling, transmitting a message to a mobile phone or a control center using the wireless transmitter.